Ephedra regional gradient (ERG) analyses

ecoblender

alex.filazzola

Abstract

An under-examined component of the shrub-annual relationship is how regional drivers, such as climate, may alter the sign or magnitude of positive interactions. Interspecific interactions between plants have been shown to be strongly linked to climate, particularly temperature and precipitation. The stress-gradient hypothesis (SGH) predicts that higher abiotic stress (i.e. for deserts, increasing temperature and reduced precipitation) will increase the frequency of positive interactions among shrubs and their annual understory. Regional climate gradients also have indirect effects on plant composition, such as determining consumer abundance and soil nutrient composition. Nutrient availability is particularly affected by precipitation because of altered decomposition rates of organic matter and mineralization. Therefore, the strength of facilitation and operating mechanism of a shrub on the annual plant community may change along a regional gradient.

Hypothesis

We tested the hypothesis that positive interactions among shrubs and annual plants will increase with abiotic stress and reduce nutrient availability along a regional gradient of aridity.

Methods

Weather for growing season

Climate patterns within study

season1.sjd <- season1 %>% filter(Gradient<4) %>% group_by(year, month,days) %>% summarise_if(is.numeric, funs(mean(., na.rm=T)))
season1.mnp <- season1 %>% filter(Gradient>3)%>% group_by(year, month,days) %>% summarise_if(is.numeric, funs(mean(., na.rm=T)))
season2.sjd <- season2 %>% filter(Gradient<4) %>% group_by(year, month,days) %>% summarise_if(is.numeric, funs(mean(., na.rm=T)))
season2.mnp <- season2 %>% filter(Gradient>3)%>% group_by(year, month,days) %>% summarise_if(is.numeric, funs(mean(., na.rm=T)))

## Rain vs Temperature in 2016
par(mfrow=c(2,1))
par(mar=c(3.5,4.5,1,4.5))
plot1 <- barplot(height=season1.sjd$Precip, ylim=c(0,14), ylab="Average precipitation San Joaquin (cm)")
points(plot1[,1], season1.sjd$min.temp, type="l", col="#FF000050", lwd=2)
axis(4, at=seq(0,14,2), lab=seq(0,14,2), ylab="")
mtext("Average temperature at all sites (°C)", 4, line=3)
par(mar=c(4.5,4.5,0,4.5))
plot1 <- barplot(height=season1.mnp$Precip, ylim=c(0,14), ylab="Average precipitation Mojave (cm)")
axis(1, plot1[c(1,30,60,90,120,150,180)], c("Nov","Dec","Jan","Feb","Mar","Apr","May"))
points(plot1[,1], season1.mnp$min.temp, type="l", col="#FF000050", lwd=2)
axis(4, at=seq(0,14,2), lab=seq(0,14,2), ylab="")
mtext("Average temperature at all sites (°C)", 4, line=3)

## Rain vs Temperature in 2017
par(mfrow=c(2,1))
par(mar=c(3.5,4.5,1,4.5))
plot2 <- barplot(height=season2.sjd$Precip, ylim=c(0,14), ylab="Average precipitation San Joaquin (cm)")
points(plot2[,1], season2.sjd$min.temp, type="l", col="#FF000050", lwd=2)
axis(4, at=seq(0,14,2), lab=seq(0,14,2), ylab="")
mtext("Average temperature San Joaquin (°C)", 4, line=3)
par(mar=c(4.5,4.5,0,4.5))
plot2 <- barplot(height=season2.mnp$Precip, ylim=c(0,14), ylab="Average precipitation Mojave (cm)")
points(plot2[,1], season2.mnp$min.temp, type="l", col="#FF000050", lwd=2)
axis(1, plot1[c(1,30,60,90,120,150,180)], c("Nov","Dec","Jan","Feb","Mar","Apr","May"))
axis(4, at=seq(0,14,2), lab=seq(0,14,2), ylab="")
mtext("Average temperature Mojave (°C)", 4, line=3)

### 2016 The rain was inconsistent and mostly absent in the Mojave. This resulted in low germination and producitivty at the southern sites
### 2017 The rain was more plentiful, but in the northern sites, there appears to be a frost period after the majority of the rainfall. Need to check number of frost days

season1.frost <- season1 %>% group_by(Site) %>% summarize(frost.days=sum(min.temp<0, na.rm=T)/length(min.temp)*100)
data.frame(season1.frost)
##              Site frost.days
## 1         Barstow  24.725275
## 2          Cuyama  29.670330
## 3   HeartofMojave  25.824176
## 4    PanocheHills  10.439560
## 5 SheepholeValley   6.043956
## 6          Tecopa  23.626374
## 7      TejonRanch  50.549451
season2.frost <- season2 %>% group_by(Site) %>% summarize(frost.days=sum(min.temp<0, na.rm=T)/length(min.temp)*100)
data.frame(season2.frost)
##              Site frost.days
## 1         Barstow  17.679558
## 2          Cuyama  19.889503
## 3   HeartofMojave  16.574586
## 4    PanocheHills  14.917127
## 5 SheepholeValley   1.785714
## 6          Tecopa  25.966851
## 7      TejonRanch  35.911602
## Both years had comparable number of frost days

## Compare number of consecutive frost days (i.e. frost periods)
season1[,"frost"] <- ifelse(season1$min.temp<0, -99,season1$min.temp) ## identified days below freezing
season2[,"frost"] <- ifelse(season2$min.temp<0, -99,season2$min.temp) ## identified days below freezing
count.consec <- function(x) {max(rle(as.character(x))$lengths)}

season1.frost <- season1 %>% group_by(Site)  %>% summarize(count.consec(frost))
data.frame(season1.frost)
##              Site count.consec.frost.
## 1         Barstow                  10
## 2          Cuyama                  10
## 3   HeartofMojave                  12
## 4    PanocheHills                   5
## 5 SheepholeValley                   7
## 6          Tecopa                  11
## 7      TejonRanch                  14
season2.frost <- season2 %>% group_by(Site) %>% summarize(count.consec(frost))
data.frame(season2.frost)
##              Site count.consec.frost.
## 1         Barstow                   5
## 2          Cuyama                   6
## 3   HeartofMojave                   7
## 4    PanocheHills                   6
## 5 SheepholeValley                   3
## 6          Tecopa                   7
## 7      TejonRanch                  13
## compare only after plants have germinated
season1.frost <- season1 %>% group_by(Site) %>% filter(year>2015) %>% summarize(frost.days=sum(min.temp<0, na.rm=T)/length(min.temp)*100, avg.min.temp=mean(min.temp, na.rm=T))
data.frame(season1.frost)
##              Site frost.days avg.min.temp
## 1         Barstow  12.396694     5.750413
## 2          Cuyama  11.570248     3.352893
## 3   HeartofMojave  16.528926     4.542149
## 4    PanocheHills   2.479339     6.777686
## 5 SheepholeValley   2.479339     9.394167
## 6          Tecopa  11.570248     6.342149
## 7      TejonRanch  33.884298     1.438017
season2.frost <- season2 %>% group_by(Site) %>%  filter(year>2016) %>%  summarize(frost.days=sum(min.temp<0, na.rm=T)/length(min.temp)*100,avg.min.temp=mean(min.temp, na.rm=T))
data.frame(season2.frost)
##              Site frost.days avg.min.temp
## 1         Barstow  11.666667     6.052500
## 2          Cuyama  16.666667     3.624167
## 3   HeartofMojave   8.333333     5.637838
## 4    PanocheHills   8.333333     6.065833
## 5 SheepholeValley   0.000000     9.386916
## 6          Tecopa  20.833333     5.938333
## 7      TejonRanch  28.333333     2.535833
season1.frost <- season1 %>% group_by(Site)  %>% filter(year>2015) %>% summarize(count.consec(frost))
data.frame(season1.frost)
##              Site count.consec.frost.
## 1         Barstow                   5
## 2          Cuyama                   6
## 3   HeartofMojave                   4
## 4    PanocheHills                   2
## 5 SheepholeValley                   2
## 6          Tecopa                   4
## 7      TejonRanch                  10
season2.frost <- season2 %>% group_by(Site)  %>%  filter(year>2016)%>% summarize(count.consec(frost))
data.frame(season2.frost)
##              Site count.consec.frost.
## 1         Barstow                   5
## 2          Cuyama                   6
## 3   HeartofMojave                   3
## 4    PanocheHills                   3
## 5 SheepholeValley                   2
## 6          Tecopa                   5
## 7      TejonRanch                  10

Microenvironmental differences

We compared temperature and relative humidity between shrub and open microsites among all sites along the regional gradient


    Shapiro-Wilk normality test

data:  fit1$residuals
W = 0.87899, p-value < 2.2e-16
                     Df Sum Sq Mean Sq F value Pr(>F)    
Microsite             1   33.6   33.59  76.013 <2e-16 ***
gradient              6  234.0   39.00  88.248 <2e-16 ***
Microsite:gradient    6    8.8    1.46   3.306  0.003 ** 
Residuals          4290 1896.0    0.44                   
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
30 observations deleted due to missingness
Analysis of Deviance Table

Model: binomial, link: logit

Response: RH/100

Terms added sequentially (first to last)

                   Df Deviance Resid. Df Resid. Dev Pr(>Chi)    
NULL                                4333     974.99             
Microsite           1     2.11      4332     972.88   0.1463    
gradient            6   528.28      4326     444.60   <2e-16 ***
Microsite:gradient  6     7.44      4320     437.17   0.2824    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Analysis of Deviance Table

Model: Gamma, link: inverse

Response: swc

Terms added sequentially (first to last)

               Df Deviance Resid. Df Resid. Dev        F Pr(>F)    
NULL                             404    231.405                    
Site            6  189.683       398     41.722 381.2179 <2e-16 ***
Microsite       1    0.287       397     41.435   3.4579 0.0637 .  
Site:Microsite  6    0.696       391     40.739   1.3994 0.2136    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Analysis of Deviance Table

Model: Gamma, link: inverse

Response: swc

Terms added sequentially (first to last)

               Df Deviance Resid. Df Resid. Dev        F Pr(>F)    
NULL                             419    183.193                    
Site            6  144.930       413     38.263 279.4537 <2e-16 ***
Microsite       1    0.080       412     38.183   0.9284 0.3358    
Site:Microsite  6    0.535       406     37.647   1.0322 0.4037    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

site level means

## site coordintes
site.gps <- read.csv("Data/ERGsites.csv")
# nutrient data for each site
nutrients <- read.csv("Data/ERG.soilnutrients.csv")
 ## long term climate data extracted from worldclim
clim.dat <- read.csv("Data/ERG.worldclim.csv")


## check correlation among long-term climate variables
cor(clim.dat[,3:8]) ## Temp.wettest.QR least correlated 
##                    Annual.Temp Temp.Seasonality Temp.wettest.QR
## Annual.Temp          1.0000000        0.9051791       0.7260617
## Temp.Seasonality     0.9051791        1.0000000       0.4650548
## Temp.wettest.QR      0.7260617        0.4650548       1.0000000
## Annual.precip       -0.8911069       -0.9650533      -0.5313460
## Precip.seasonality  -0.9715419       -0.9774646      -0.6165170
## Precipt.wettest.QR  -0.8839124       -0.9570957      -0.5523347
##                    Annual.precip Precip.seasonality Precipt.wettest.QR
## Annual.Temp           -0.8911069         -0.9715419         -0.8839124
## Temp.Seasonality      -0.9650533         -0.9774646         -0.9570957
## Temp.wettest.QR       -0.5313460         -0.6165170         -0.5523347
## Annual.precip          1.0000000          0.9613729          0.9980959
## Precip.seasonality     0.9613729          1.0000000          0.9543657
## Precipt.wettest.QR     0.9980959          0.9543657          1.0000000
## Obtain mean shrub traits for each site
shrubs <- read.csv("Data/ERG.shrub.csv")
shrubs <- subset(shrubs, Microsite=="shrub")
shrubs.mean <- shrubs %>% group_by(Gradient,Site) %>% summarise_all(funs(mean))
shrubs.mean <- data.frame(shrubs.mean)
shrubs.vars <- shrubs.mean[,c("Site","Gradient","volume","canopy","Dx","DxEph","Compaction")] 

## test correlation among shrub traits
cor(shrubs.vars[,3:7]) ## compaction x volume & DxEph x Dx high correlation
##                volume     canopy         Dx      DxEph Compaction
## volume      1.0000000  0.2243902  0.4003665  0.2631563 -0.7023062
## canopy      0.2243902  1.0000000 -0.6847957 -0.3538094 -0.1437590
## Dx          0.4003665 -0.6847957  1.0000000  0.7803527 -0.1355700
## DxEph       0.2631563 -0.3538094  0.7803527  1.0000000  0.3221944
## Compaction -0.7023062 -0.1437590 -0.1355700  0.3221944  1.0000000
vifstep(shrubs.vars[,3:7], th=10) ## Dx showing collinearity problems
## 1 variables from the 5 input variables have collinearity problem: 
##  
## Dx 
## 
## After excluding the collinear variables, the linear correlation coefficients ranges between: 
## min correlation ( Compaction ~ canopy ):  -0.143759 
## max correlation ( Compaction ~ volume ):  -0.7023062 
## 
## ---------- VIFs of the remained variables -------- 
##    Variables      VIF
## 1     volume 7.026144
## 2     canopy 1.925499
## 3      DxEph 4.316058
## 4 Compaction 6.400914
shrub.site <- shrubs.vars[,-c(1,2,5)]
rownames(shrub.site) <- shrubs.vars[,1]

## PCA of shrub characteristics
pca1 <- prcomp(log(shrub.site), scale=T)
plot(pca1)

biplot(pca1, scale = T)

summary(pca1) ## 77% variation explained
## Importance of components:
##                           PC1    PC2    PC3    PC4
## Standard deviation     1.3017 1.1778 0.9082 0.3059
## Proportion of Variance 0.4236 0.3468 0.2062 0.0234
## Cumulative Proportion  0.4236 0.7704 0.9766 1.0000
## PCA of site characteristics for season1
## Obtain mean weather variables for each site
season1.mean <- season1 %>% group_by(Gradient,Site) %>% summarise(temp.var=var(avg.temp,na.rm=T),Precip=sum(Precip),wind=mean(wind.speed, na.rm=T))
season1.mean  <- data.frame(season1.mean)
## extract key variables
## dropped RH, and chose min.temp because least correlation with others & cold stress

##combine nutrients and long-term averages
site.vars <- data.frame(season1.mean[,3:5],site.gps["elevation"]) ## drop Phosphorus and other climate variables because of correlations, 
row.names(site.vars) <- shrubs.vars[,1]
cor(site.vars)
##              temp.var     Precip        wind  elevation
## temp.var   1.00000000 -0.8679054  0.05816084 -0.2699588
## Precip    -0.86790537  1.0000000 -0.37279135  0.1222504
## wind       0.05816084 -0.3727914  1.00000000  0.1054497
## elevation -0.26995879  0.1222504  0.10544974  1.0000000
site.vars2016 <- site.vars

##  check for collinearity
vifstep(site.vars, th=10) ## remove potassium and temperature minimum
## No variable from the 4 input variables has collinearity problem. 
## 
## The linear correlation coefficients ranges between: 
## min correlation ( wind ~ temp.var ):  0.05816084 
## max correlation ( Precip ~ temp.var ):  -0.8679054 
## 
## ---------- VIFs of the remained variables -------- 
##   Variables      VIF
## 1  temp.var 6.640710
## 2    Precip 7.316232
## 3      wind 1.739558
## 4 elevation 1.142681
pca1 <- prcomp(log(abs(site.vars)), scale=T)
plot(pca1)

biplot(pca1)

## check contribution of loadings
pca1$rotation
##                  PC1        PC2         PC3        PC4
## temp.var   0.6001002 -0.1669018  0.50554765 -0.5970303
## Precip    -0.6703313 -0.1105034 -0.09936844 -0.7270288
## wind       0.2743589  0.7981791 -0.43410209 -0.3149488
## elevation -0.3395039  0.5681927  0.73898773  0.1256633
aload <- abs(pca1$rotation)
sweep(aload, 2, colSums(aload), "/")
##                 PC1       PC2        PC3        PC4
## temp.var  0.3184748 0.1015355 0.28433406 0.33832382
## Precip    0.3557466 0.0672253 0.05588758 0.41199111
## wind      0.1456030 0.4855763 0.24415109 0.17847449
## elevation 0.1801756 0.3456629 0.41562726 0.07121058
summary(pca1) ## 89% variation explained
## Importance of components:
##                          PC1    PC2    PC3     PC4
## Standard deviation     1.456 1.0458 0.8577 0.22479
## Proportion of Variance 0.530 0.2734 0.1839 0.01263
## Cumulative Proportion  0.530 0.8034 0.9874 1.00000
gradient1.season1 <- pca1$x[,1]
gradient2.season1 <- pca1$x[,2]

## season2
season2.mean <- season2 %>% group_by(Gradient,Site) %>% summarise(temp.var=var(avg.temp,na.rm=T),Precip=sum(Precip, na.rm=T),wind=mean(wind.speed, na.rm=T))
season2.mean  <- data.frame(season2.mean)
## extract key variables
## dropped RH, and chose min.temp because least correlation with others & cold stress

##combine nutrients and long-term averages
site.vars <- data.frame(season2.mean[,3:5],site.gps["elevation"]) ## drop Phosphorus and other climate variables because of correlations, 
row.names(site.vars) <-  shrubs.vars[,1]
cor(site.vars)
##             temp.var     Precip       wind  elevation
## temp.var   1.0000000 -0.7427028 -0.2262409 -0.3715471
## Precip    -0.7427028  1.0000000 -0.1551517  0.1813600
## wind      -0.2262409 -0.1551517  1.0000000  0.1052805
## elevation -0.3715471  0.1813600  0.1052805  1.0000000
site.vars2017 <- site.vars


##  check for collinearity
vifstep(site.vars, th=10) ## remove potassium and temperature minimum
## No variable from the 4 input variables has collinearity problem. 
## 
## The linear correlation coefficients ranges between: 
## min correlation ( elevation ~ wind ):  0.1052805 
## max correlation ( Precip ~ temp.var ):  -0.7427028 
## 
## ---------- VIFs of the remained variables -------- 
##   Variables      VIF
## 1  temp.var 3.438786
## 2    Precip 3.035337
## 3      wind 1.402010
## 4 elevation 1.192010
pca2 <- prcomp(log(abs(site.vars)), scale=T)
plot(pca2)

biplot(pca2)

summary(pca2) ## 85% variation explained
## Importance of components:
##                          PC1    PC2    PC3     PC4
## Standard deviation     1.434 1.0121 0.8809 0.37883
## Proportion of Variance 0.514 0.2561 0.1940 0.03588
## Cumulative Proportion  0.514 0.7701 0.9641 1.00000
## check contribution of loadings
pca2$rotation
##                  PC1         PC2         PC3       PC4
## temp.var   0.6557532 -0.09123357  0.20565681 0.7206729
## Precip    -0.6176537 -0.16891771 -0.40105332 0.6550778
## wind      -0.1019884  0.97279862  0.06826009 0.1964733
## elevation -0.4220070 -0.12963831  0.89005734 0.1135866
aload <- abs(pca2$rotation)
sweep(aload, 2, colSums(aload), "/")
##                  PC1        PC2       PC3        PC4
## temp.var  0.36483385 0.06695609 0.1314078 0.42749339
## Precip    0.34363685 0.12396828 0.2562596 0.38858328
## wind      0.05674213 0.71393442 0.0436159 0.11654531
## elevation 0.23478717 0.09514122 0.5687167 0.06737801
## define gradients
gradient1.season2 <- pca2$x[,1]
gradient2.season2 <- pca2$x[,2]

Compare gradient to phytometer

# crs.world <-CRS("+proj=longlat +datum=WGS84")
# gps <- data.frame(x=site.gps$long,y=site.gps$lat)
# coordinates(gps) <- ~x+y
# proj4string(gps) <- crs.world
# 
# ## Download and extract PET/aridity
# r1 <- raster("C:\\Users\\Alessandro\\Downloads\\Global Aridity - Annual\\AI_annual\\ai_yr\\w001001.adf", package="raster") #aridity
# r2 <- raster("C:\\Users\\Alessandro\\Downloads\\Global PET - Annual\\PET_he_annual\\pet_he_yr\\w001001.adf", package="raster") #potential evapotranspiration
# calclim <- stack(r1,r2)
# names(calclim) <- c("aridity","PET")
# arid.vals <- raster::extract(calclim, gps)
# rownames(arid.vals) <- site.gps[,"name"]
# colnames(arid.vals)[1] <- "Gradient"
# write.csv(arid.vals, "Data/aridity.PET.csv")
arid.vals <- read.csv("Data/aridity.PET.csv")
colnames(arid.vals)[1] <- "Site"


par(mfrow=c(1,2))
mean.phyto <- census %>% filter(Census=="end") %>%group_by(Year, Gradient, Site, Microsite) %>% summarise_if(is.numeric, funs(mean(., na.rm=T)))
mean.phyto <- data.frame(mean.phyto)

rii.data <- rii(census, 1:6, var=9:15)
rii.mean <- rii.data %>% group_by(Year, Gradient,Site) %>% summarise_if(is.numeric, funs(mean(., na.rm=T)))
rii.mean <- data.frame(rii.mean)

season1.mean <- season1 %>% group_by(Gradient,Site) %>% summarise(temp.var=var(avg.temp,na.rm=T),Precip=sum(Precip),wind=mean(wind.speed, na.rm=T), max.temp=abs(mean(max.temp, na.rm=T)),min.temp=abs(mean(min.temp, na.rm=T)),avg.temp=abs(mean(avg.temp, na.rm=T)))
s1.mean <- data.frame(season1.mean)

season2.mean <- season2 %>% group_by(Gradient,Site) %>% summarise(temp.var=var(avg.temp,na.rm=T),Precip=sum(Precip, na.rm=T),wind=mean(wind.speed, na.rm=T), max.temp=abs(mean(max.temp, na.rm=T)),min.temp=abs(mean(min.temp, na.rm=T)),avg.temp=abs(mean(avg.temp, na.rm=T)))
s2.mean <- data.frame(season2.mean)


s1.mean[,"arid.gradient"] <- log(s1.mean[,"Precip"]/arid.vals[,"PET"])
s2.mean[,"arid.gradient"] <- log(s2.mean[,"Precip"]/(arid.vals[,"PET"]))

## collect aridity gradient data
site.climate <- rbind(s1.mean,s2.mean)
site.climate[,"Year"] <- as.factor(c(rep("2016",7),rep("2017",7)))

## combine climate data with census
mean.phyto  <- merge(mean.phyto , site.climate, by=c("Year","Site"))

phyto.2016 <- subset(mean.phyto, Year==2016)

plot(jitter(phyto.2016[phyto.2016$Microsite=="open","arid.gradient"],1), phyto.2016[phyto.2016$Microsite=="open","phyto.biomass"], ylim=c(0,2), ylab=c("all phytometer biomass (g)"), xlab=c("aridity gradient"))
points(jitter(phyto.2016[phyto.2016$Microsite=="open","arid.gradient"],2), phyto.2016[phyto.2016$Microsite=="shrub","phyto.biomass"], pch=19)
legend(-4.5,1.8, c("shrub","open"), pch=c(19,1))
text(-2.3, 1.8, "2016", cex=1.5)

plot(phyto.2016[phyto.2016$Microsite=="open","arid.gradient"], rii.mean[rii.mean$Year==2016,"phyto.biomass"], ylim=c(-0.4,0.4), ylab="RII all phytometer biomass", pch=19, xlab=c("aridity gradient"))
text(-2.3, 0.38, "2016", cex=1.5)

phyto.2017 <- subset(mean.phyto, Year==2017)

plot(jitter(phyto.2017[phyto.2017$Microsite=="open","arid.gradient"],1), phyto.2017[phyto.2017$Microsite=="open","phyto.biomass"], ylim=c(0,3), xlab=c("aridity gradient"),ylab=c("all phytometer biomass (g)"))
points(jitter(phyto.2017[phyto.2017$Microsite=="open","arid.gradient"],2), phyto.2017[phyto.2017$Microsite=="shrub","phyto.biomass"], pch=19)
legend(-4.5,1.8, c("shrub","open"), pch=c(19,1))
text(-2.0, 2.8, "2017", cex=1.5)

plot(phyto.2017[phyto.2017$Microsite=="open","arid.gradient"], rii.mean[rii.mean$Year==2017,"phyto.biomass"], ylim=c(-0.4,0.4), ylab="RII all phytometer biomass", pch=19, xlab=c("aridity gradient"))
text(-2.0, 0.38, "2017", cex=1.5)

## regress gradient and microsite on biomass for phytometers in 2016
m1 <- lm(ihs(Phacelia.biomass)~arid.gradient * Microsite, data=phyto.2016)
summary(m1) ## nothing significant
## 
## Call:
## lm(formula = ihs(Phacelia.biomass) ~ arid.gradient * Microsite, 
##     data = phyto.2016)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.26646 -0.16692  0.01821  0.09053  0.43991 
## 
## Coefficients:
##                              Estimate Std. Error t value Pr(>|t|)  
## (Intercept)                   0.75896    0.31899   2.379   0.0387 *
## arid.gradient                 0.16726    0.09147   1.829   0.0974 .
## Micrositeshrub                0.81930    0.45112   1.816   0.0994 .
## arid.gradient:Micrositeshrub  0.19010    0.12936   1.470   0.1724  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.2383 on 10 degrees of freedom
## Multiple R-squared:  0.674,  Adjusted R-squared:  0.5762 
## F-statistic: 6.893 on 3 and 10 DF,  p-value: 0.008498
m2 <- lm(ihs(Plantago.biomass)~arid.gradient * Microsite, data=phyto.2016)
summary(m2) ## nothing significant
## 
## Call:
## lm(formula = ihs(Plantago.biomass) ~ arid.gradient * Microsite, 
##     data = phyto.2016)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.070995 -0.048467  0.003316  0.024049  0.115472 
## 
## Coefficients:
##                              Estimate Std. Error t value Pr(>|t|)   
## (Intercept)                   0.27085    0.07901   3.428  0.00646 **
## arid.gradient                 0.05886    0.02266   2.598  0.02658 * 
## Micrositeshrub               -0.12531    0.11174  -1.121  0.28831   
## arid.gradient:Micrositeshrub -0.02928    0.03204  -0.914  0.38239   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.05904 on 10 degrees of freedom
## Multiple R-squared:  0.4794, Adjusted R-squared:  0.3232 
## F-statistic: 3.069 on 3 and 10 DF,  p-value: 0.07774
m3 <- lm(ihs(Salvia.biomass)~arid.gradient * Microsite, data=phyto.2016)
summary(m3)## aridity significant
## 
## Call:
## lm(formula = ihs(Salvia.biomass) ~ arid.gradient * Microsite, 
##     data = phyto.2016)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.15051 -0.09099 -0.01568  0.03332  0.26045 
## 
## Coefficients:
##                               Estimate Std. Error t value Pr(>|t|)   
## (Intercept)                   0.748316   0.194293   3.851   0.0032 **
## arid.gradient                 0.159284   0.055714   2.859   0.0170 * 
## Micrositeshrub               -0.000770   0.274771  -0.003   0.9978   
## arid.gradient:Micrositeshrub  0.004763   0.078792   0.060   0.9530   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.1452 on 10 degrees of freedom
## Multiple R-squared:  0.6281, Adjusted R-squared:  0.5165 
## F-statistic:  5.63 on 3 and 10 DF,  p-value: 0.01599
## regress gradient and microsite on biomass for phytometers in 2017
m4 <- lm(ihs(Phacelia.biomass)~arid.gradient * Microsite, data=phyto.2017)
summary(m4) ## nothing significant
## 
## Call:
## lm(formula = ihs(Phacelia.biomass) ~ arid.gradient * Microsite, 
##     data = phyto.2017)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.53579 -0.12720  0.00415  0.29889  0.40793 
## 
## Coefficients:
##                              Estimate Std. Error t value Pr(>|t|)
## (Intercept)                    -6.725      3.895  -1.727    0.135
## arid.gradient                  -2.514      1.315  -1.912    0.104
## Micrositeshrub                  6.929      4.012   1.727    0.135
## arid.gradient:Micrositeshrub    2.341      1.363   1.718    0.137
## 
## Residual standard error: 0.4168 on 6 degrees of freedom
##   (4 observations deleted due to missingness)
## Multiple R-squared:  0.3953, Adjusted R-squared:  0.09289 
## F-statistic: 1.307 on 3 and 6 DF,  p-value: 0.3557
m5 <- lm(ihs(Plantago.biomass)~arid.gradient * Microsite, data=phyto.2017)
summary(m5) ## aridity significant
## 
## Call:
## lm(formula = ihs(Plantago.biomass) ~ arid.gradient * Microsite, 
##     data = phyto.2017)
## 
## Residuals:
##        15        16        19        20        23        24        25 
## -0.008390 -0.070144  0.006678 -0.031325  0.126617  0.249998 -0.124905 
##        26 
## -0.148529 
## 
## Coefficients:
##                              Estimate Std. Error t value Pr(>|t|)
## (Intercept)                   -1.4847     1.6331  -0.909    0.415
## arid.gradient                 -0.6486     0.5513  -1.177    0.305
## Micrositeshrub                 3.1150     2.3096   1.349    0.249
## arid.gradient:Micrositeshrub   1.1087     0.7796   1.422    0.228
## 
## Residual standard error: 0.1748 on 4 degrees of freedom
##   (6 observations deleted due to missingness)
## Multiple R-squared:  0.4909, Adjusted R-squared:  0.109 
## F-statistic: 1.285 on 3 and 4 DF,  p-value: 0.3935
m6 <- lm(ihs(Salvia.biomass)~arid.gradient * Microsite, data=phyto.2017)
summary(m6)## nothing significant
## 
## Call:
## lm(formula = ihs(Salvia.biomass) ~ arid.gradient * Microsite, 
##     data = phyto.2017)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.52276 -0.13176 -0.02786  0.05787  0.53706 
## 
## Coefficients:
##                              Estimate Std. Error t value Pr(>|t|)
## (Intercept)                   -0.5442     0.6266  -0.869    0.405
## arid.gradient                 -0.4200     0.2423  -1.733    0.114
## Micrositeshrub                 0.2852     0.8861   0.322    0.754
## arid.gradient:Micrositeshrub   0.1488     0.3427   0.434    0.673
## 
## Residual standard error: 0.335 on 10 degrees of freedom
## Multiple R-squared:  0.3112, Adjusted R-squared:  0.1046 
## F-statistic: 1.506 on 3 and 10 DF,  p-value: 0.2723
# 
# ## Phacelia season 1
# plot(jitter(phyto.2016[phyto.2016$Microsite=="open","arid.gradient"] ,1), phyto.2016[phyto.2016$Microsite=="open","Phacelia.biomass"], ylim=c(0,2))
# points(jitter(phyto.2016[phyto.2016$Microsite=="shrub","arid.gradient"] ,2), phyto.2016[phyto.2016$Microsite=="shrub","Phacelia.biomass"], pch=19)
# 
# m1 <- lm(ihs(Phacelia.biomass)~arid.gradient * Microsite, data=phyto.2016)
# m2 <- lm(ihs(Plantago.biomass)~arid.gradient * Microsite, data=phyto.2016)
# m3 <- lm(ihs(Salvia.biomass)~arid.gradient * Microsite, data=phyto.2016)
# 
# plot(phyto.2016[phyto.2016$Microsite=="open","arid.gradient"] , rii.mean[rii.mean$Year==2016,"Phacelia.biomass"], ylim=c(-0.4,0.4))
# 
# ## Plantago season 1
# plot(jitter(phyto.2016[phyto.2016$Microsite=="open","arid.gradient"] ,1), phyto.2016[phyto.2016$Microsite=="open","Plantago.biomass"], ylim=c(0,2))
# points(jitter(phyto.2016[phyto.2016$Microsite=="open","arid.gradient"] ,2), phyto.2016[phyto.2016$Microsite=="shrub","Plantago.biomass"], pch=19)
# 
# plot(phyto.2016[phyto.2016$Microsite=="open","arid.gradient"] , rii.mean[rii.mean$Year==2016,"Plantago.biomass"], ylim=c(-0.4,0.4))
# 
# ## Salvia season 1
# plot(jitter(phyto.2016[phyto.2016$Microsite=="open","arid.gradient"] ,1), phyto.2016[phyto.2016$Microsite=="open","Salvia.biomass"], ylim=c(0,2))
# points(jitter(phyto.2016[phyto.2016$Microsite=="open","arid.gradient"] ,2), phyto.2016[phyto.2016$Microsite=="shrub","Salvia.biomass"], pch=19)
# 
# plot(phyto.2016[phyto.2016$Microsite=="open","arid.gradient"] , rii.mean[rii.mean$Year==2016,"Salvia.biomass"], ylim=c(-0.4,0.4))
# 
# 
# 
# ## Phacelia season 2
# plot(jitter(gradient1.season2,1), phyto.2017[phyto.2017$Microsite=="open","Phacelia.biomass"], ylim=c(0,2))
# points(jitter(gradient1.season2,2), phyto.2017[phyto.2017$Microsite=="shrub","Phacelia.biomass"], pch=19)
# 
# plot(gradient1.season2, rii.mean[rii.mean$Year==2017,"Phacelia.biomass"], ylim=c(-0.4,0.4))
# 
# 
# ## Plantago season 2
# plot(jitter(gradient1.season2,1), phyto.2017[phyto.2017$Microsite=="open","Plantago.biomass"], ylim=c(0,2))
# points(jitter(gradient1.season2,2), phyto.2017[phyto.2017$Microsite=="shrub","Plantago.biomass"], pch=19)
# 
# plot(gradient1.season2, rii.mean[rii.mean$Year==2017,"Plantago.biomass"], ylim=c(-0.4,0.4))
# 
# ## Salvia season 2
# plot(jitter(gradient1.season2,1), phyto.2017[phyto.2017$Microsite=="open","Salvia.biomass"], ylim=c(0,2))
# points(jitter(gradient1.season2,2), phyto.2017[phyto.2017$Microsite=="shrub","Salvia.biomass"], pch=19)
# 
# 
# plot(gradient1.season2, rii.mean[rii.mean$Year==2017,"Salvia.biomass"], ylim=c(-0.4,0.4))


## both seasons


## responses
plot(mean.phyto[mean.phyto$Microsite=="open","arid.gradient"], mean.phyto[mean.phyto$Microsite=="open","phyto.biomass"], ylim=c(0,3), ylab=c("all phytometer biomass (g)"))
points(jitter(mean.phyto[mean.phyto$Microsite=="shrub","arid.gradient"],1), mean.phyto[mean.phyto$Microsite=="shrub","phyto.biomass"], pch=19)
legend(-4.5,2.8, c("shrub","open"), pch=c(19,1))
text(-2.3, 2.8, "both years", cex=1.3)

# 
# ## Phacelia
# plot(jitter(mean.phyto[mean.phyto$Microsite=="open","arid.gradient"],1), mean.phyto[mean.phyto$Microsite=="open","Phacelia.biomass"], ylim=c(0,2))
# points(jitter(mean.phyto[mean.phyto$Microsite=="shrub","arid.gradient"],2), mean.phyto[mean.phyto$Microsite=="shrub","Phacelia.biomass"], pch=19)
# m1 <- lm(ihs(Phacelia.biomass) ~ arid.gradient * Microsite, data=mean.phyto)
# summary(m1)
# 
# ## Plantago 
# plot(jitter(mean.phyto[mean.phyto$Microsite=="open","arid.gradient"],1), mean.phyto[mean.phyto$Microsite=="open","Plantago.biomass"], ylim=c(0,0.5))
# points(jitter(mean.phyto[mean.phyto$Microsite=="shrub","arid.gradient"],2), mean.phyto[mean.phyto$Microsite=="shrub","Plantago.biomass"], pch=19)
# m2 <- lm(ihs(Phacelia.biomass) ~ arid.gradient * Microsite, data=mean.phyto)
# summary(m2)
# 
# 
# ## Salvia season 1
# plot(jitter(mean.phyto[mean.phyto$Microsite=="open","arid.gradient"],1), mean.phyto[mean.phyto$Microsite=="open","Salvia.biomass"], ylim=c(0,2))
# points(jitter(mean.phyto[mean.phyto$Microsite=="shrub","arid.gradient"],2), mean.phyto[mean.phyto$Microsite=="shrub","Salvia.biomass"], pch=19)
# m3 <- lm(ihs(Salvia.biomass) ~ arid.gradient * Microsite, data=mean.phyto)
# summary(m3)



### Rii plots

rii.mean <- merge(rii.mean, site.climate, by=c("Year","Site"))


## biomass by RII
plot(mean.phyto[mean.phyto$Microsite=="open","Phacelia.biomass"], rii.mean[,"Phacelia.biomass"], ylim=c(-0.3,0.3), pch=19, ylab="RII biomass", xlab="average phytometer biomass per site")
points(mean.phyto[mean.phyto$Microsite=="open","Plantago.biomass"], rii.mean[,"Plantago.biomass"], pch=21, bg="blue")
points(mean.phyto[mean.phyto$Microsite=="open","Salvia.biomass"], rii.mean[,"Salvia.biomass"], pch=21, bg="red")
abline(h=0, lty=2, lwd=2)
legend(0.38,-0.15, c("Phacelia","Plantago","Salvia"), pch=21, pt.bg=c("black","blue","red"))

## rii variability


plot(rii.mean[,"arid.gradient"], rii.mean[,"Phacelia.biomass"], ylim=c(-0.5,0.5), pch=19, ylab="Rii phytometer biomass", xlab="aridity gradient")
points(rii.mean[,"arid.gradient"], rii.mean[,"Plantago.biomass"], pch=21, bg="blue")
points(rii.mean[,"arid.gradient"], rii.mean[,"Salvia.biomass"], pch=21, bg="red")
abline(h=0, lty=2, lwd=2)
legend(-4.5, 0.45, c("Phacelia","Plantago","Salvia"), pch=21, pt.bg=c("black","blue","red"))

## compare convex hull
## hull function
Plot_ConvexHull<-function(xcoord, ycoord, lcolor){
  hpts <- chull(x = xcoord, y = ycoord)
  hpts <- c(hpts, hpts[1])
  lines(xcoord[hpts], ycoord[hpts], col = lcolor)
}  

Plot_ConvexHull(xcoord = rii.mean[,"arid.gradient"], ycoord = rii.mean[,"Phacelia.biomass"], lcolor = "black")
Plot_ConvexHull(xcoord = rii.mean[,"arid.gradient"], ycoord = rii.mean[,"Plantago.biomass"], lcolor = "blue")
Plot_ConvexHull(xcoord = rii.mean[,"arid.gradient"], ycoord = rii.mean[,"Salvia.biomass"], lcolor = "red")


r.df <- rii.mean[,c("arid.gradient","Phacelia.biomass","Plantago.biomass","Salvia.biomass")]


## transform data into long format
r.df <- gather(r.df, species, biomass, 2:4)


## function to create a convex hull around data
hull.time <- function(df){
  df[chull(df$arid.gradient,df$biomass),]  # chull really is useful, even outside of contrived examples.
  }

## break data into lists and recombined to apply polygon
splitData <- split(r.df, r.df$species)
appliedData <- lapply(splitData, hull.time)
combinedData <- do.call(rbind, appliedData)

## create polygon 
poly1 <- ggplot(r.df, aes(x=arid.gradient, y=biomass))+
geom_polygon(data = combinedData,  # This is also a nice example of how to plot
                          aes(x=arid.gradient, y=biomass, group = species, color=species), fill=NA,  # two superimposed geoms
                          alpha = 1/2)  + theme_bw()    
poly1


## regress rii on gradient
m1 <- lm(Phacelia.biomass~arid.gradient, data=rii.mean)
summary(m1) ## not significant
## 
## Call:
## lm(formula = Phacelia.biomass ~ arid.gradient, data = rii.mean)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.022533 -0.013199 -0.005837  0.008594  0.058681 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)  
## (Intercept)   0.038659   0.020960   1.844   0.0899 .
## arid.gradient 0.008913   0.006828   1.305   0.2162  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.02266 on 12 degrees of freedom
## Multiple R-squared:  0.1244, Adjusted R-squared:  0.05139 
## F-statistic: 1.704 on 1 and 12 DF,  p-value: 0.2162
m2 <- lm(Plantago.biomass~arid.gradient, data=rii.mean)
summary(m2) ## not significant
## 
## Call:
## lm(formula = Plantago.biomass ~ arid.gradient, data = rii.mean)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.030560 -0.002438  0.002575  0.006742  0.019216 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)
## (Intercept)   -0.006458   0.014218  -0.454    0.658
## arid.gradient -0.001721   0.004632  -0.372    0.717
## 
## Residual standard error: 0.01537 on 12 degrees of freedom
## Multiple R-squared:  0.01137,    Adjusted R-squared:  -0.07101 
## F-statistic: 0.1381 on 1 and 12 DF,  p-value: 0.7167
m3 <- lm(Salvia.biomass~arid.gradient, data=rii.mean)
summary(m3) ## not significant
## 
## Call:
## lm(formula = Salvia.biomass ~ arid.gradient, data = rii.mean)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.051586 -0.015918 -0.001203  0.007707  0.066895 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)
## (Intercept)   -0.015346   0.029966  -0.512    0.618
## arid.gradient -0.004097   0.009762  -0.420    0.682
## 
## Residual standard error: 0.0324 on 12 degrees of freedom
## Multiple R-squared:  0.01447,    Adjusted R-squared:  -0.06766 
## F-statistic: 0.1761 on 1 and 12 DF,  p-value: 0.6821
# 
# m1 <- lm(Phacelia.biomass~arid.gradient, data=subset(rii.mean,Year == 2016))
# m2 <- lm(Plantago.biomass~arid.gradient, data=subset(rii.mean,Year == 2016))
# m3 <- lm(Salvia.biomass~arid.gradient, data=subset(rii.mean,Year == 2016))
# 
# 
# m1 <- lm(Phacelia.biomass~arid.gradient, data=subset(rii.mean,Year == 2017))
# m2 <- lm(Plantago.biomass~arid.gradient, data=subset(rii.mean,Year == 2017))
# m3 <- lm(Salvia.biomass~arid.gradient, data=subset(rii.mean,Year == 2017))
# 
# 
# plot(rii.mean[,"min.temp"], rii.mean[,"Phacelia.biomass"], ylim=c(-0.4,0.4), pch=19)
# points(rii.mean[,"min.temp"], rii.mean[,"Plantago.biomass"], pch=21, bg="blue")
# points(rii.mean[,"min.temp"], rii.mean[,"Salvia.biomass"], pch=21, bg="red")
# abline(h=0, lty=2, lwd=2)
# 
# rii.test <- subset(rii.mean, Gradient.x>3 & Year == 2017 | Year == 2016)
# 
# 
# plot(rii.test[,"arid.gradient"], rii.test[,"Phacelia.biomass"], ylim=c(-0.4,0.4), pch=19)
# points(rii.test[,"arid.gradient"], rii.test[,"Plantago.biomass"], pch=21, bg="blue")
# points(rii.test[,"arid.gradient"], rii.test[,"Salvia.biomass"], pch=21, bg="red")
# abline(h=0, lty=2, lwd=2)
# 
# 
# plot(rii.mean[,"arid.gradient"], rii.mean[,"Phacelia"], ylim=c(-0.4,0.4), pch=19)
# points(rii.mean[,"arid.gradient"], rii.mean[,"Plantago"], pch=21, bg="blue")
# points(rii.mean[,"arid.gradient"], rii.mean[,"Salvia"], pch=21, bg="red")
# abline(h=0, lty=2, lwd=2)
# 
# 
# m1 <- lm(Phacelia~ poly(arid.gradient,2), data=rii.mean)
# summary(m1)
# m2 <- lm(Plantago.biomass~arid.gradient, data=rii.mean)
# m3 <- lm(Salvia.biomass~arid.gradient, data=rii.mean)

Differences from Nutrient content

## join aridity with nutrients
nutrients.climate <- merge(nutrients,subset(site.climate, Year==2016), by=c("Site"))


## Nitrogen difference between shrub and sites
m.nit <- aov(log(N) ~ Site * microsite, data=nutrients)
summary(m.nit)
##                Df Sum Sq Mean Sq F value   Pr(>F)    
## Site            6  63.04   10.51  13.241 2.86e-09 ***
## microsite       1  40.16   40.16  50.614 2.24e-09 ***
## Site:microsite  6   4.96    0.83   1.042    0.408    
## Residuals      56  44.43    0.79                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(m.nit, "microsite")
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = log(N) ~ Site * microsite, data = nutrients)
## 
## $microsite
##                diff      lwr      upr p adj
## shrub-open 1.514873 1.088317 1.941429     0
## Potassium difference between shrub and sites
m.pot <- aov(log(K) ~ Site * microsite, data=nutrients)
summary(m.pot)
##                Df Sum Sq Mean Sq F value   Pr(>F)    
## Site            6 15.897   2.650  22.013 4.09e-13 ***
## microsite       1  4.445   4.445  36.934 1.14e-07 ***
## Site:microsite  6  1.605   0.268   2.223   0.0541 .  
## Residuals      56  6.740   0.120                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(m.pot, "microsite")
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = log(K) ~ Site * microsite, data = nutrients)
## 
## $microsite
##                 diff       lwr       upr p adj
## shrub-open 0.5040081 0.3378755 0.6701406 1e-07
## Phosphorus difference between shrub and sites
m.pho <- aov(log(P) ~ Site * microsite, data=nutrients)
summary(m.pho)
##                Df Sum Sq Mean Sq F value   Pr(>F)    
## Site            6 23.163   3.861   14.33 8.10e-10 ***
## microsite       1 10.546  10.546   39.15 5.79e-08 ***
## Site:microsite  6  3.394   0.566    2.10   0.0676 .  
## Residuals      56 15.085   0.269                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(m.pho, "microsite")
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = log(P) ~ Site * microsite, data = nutrients)
## 
## $microsite
##                 diff       lwr      upr p adj
## shrub-open 0.7762734 0.5277317 1.024815 1e-07
nutrient.mean <- nutrients.climate %>% group_by(Site, arid.gradient) %>% summarize(nitrogen=mean(N), phosphorus=mean(P), potassium=mean(K))
nutrient.mean <- data.frame(nutrient.mean)
 

ggplot(nutrient.mean) + geom_jitter(aes(x=arid.gradient, y=nitrogen), color="#E69F00", size=3) +  stat_smooth(method="lm", formula= y~poly(x,2),aes(x=arid.gradient, y=nitrogen), color="#E69F00") + geom_jitter(aes(x=arid.gradient, y=potassium/20), color="#56B4E9", size=3) +  stat_smooth(method="lm", formula= y~poly(x,2),aes(x=arid.gradient, y=potassium/20), color="#56B4E9") + geom_jitter(aes(x=arid.gradient, y=phosphorus), color="#009E73", size=3) +ylab("soil nutrient content")+ theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),axis.line = element_line(colour = "black"),panel.background = element_blank(),axis.text=element_text(size=14),axis.title=element_text(size=16)) + xlab("aridity gradient")+
annotate("segment", x = -4.5, xend = -4.35, y = 25, yend = 25,  colour = "#E69F00", size=2) + ## add custom legend
  annotate("text", x = -4.3, y = 25,  label = "Nitrogen", size=5, hjust=0)+
annotate("segment", x = -4.5, xend = -4.35, y = 23, yend = 23,  colour = "#56B4E9", size=2) + ## add custom legend
  annotate("text", x = -4.3, y = 23,  label = "Potassium", size=5, hjust=0)+
annotate("segment", x = -4.5, xend = -4.35, y = 21, yend = 21,  colour = "#009E73", size=2) + ## add custom legend
  annotate("text", x = -4.3, y = 21,  label = "Phosphorus", size=5, hjust=0)

m1 <- lm(nitrogen~poly(arid.gradient,2), data=nutrient.mean)
summary(m1)
## 
## Call:
## lm(formula = nitrogen ~ poly(arid.gradient, 2), data = nutrient.mean)
## 
## Residuals:
##       1       2       3       4       5       6       7 
##  1.1092  1.0570 -0.4565 -0.8511 -0.8789 -0.3757  0.3960 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)    
## (Intercept)               5.4326     0.3942  13.782 0.000161 ***
## poly(arid.gradient, 2)1 -12.6364     1.0429 -12.116 0.000266 ***
## poly(arid.gradient, 2)2   7.0832     1.0429   6.792 0.002454 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.043 on 4 degrees of freedom
## Multiple R-squared:  0.9797, Adjusted R-squared:  0.9695 
## F-statistic: 96.47 on 2 and 4 DF,  p-value: 0.0004126
m2 <- lm(phosphorus~arid.gradient, data=nutrient.mean)
summary(m2)
## 
## Call:
## lm(formula = phosphorus ~ arid.gradient, data = nutrient.mean)
## 
## Residuals:
##       1       2       3       4       5       6       7 
##  4.3035  1.1485 -2.0485  1.1930 -0.2791 -5.5020  1.1845 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)  
## (Intercept)     14.191      4.527   3.135   0.0258 *
## arid.gradient    1.780      1.298   1.371   0.2287  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 3.383 on 5 degrees of freedom
## Multiple R-squared:  0.2732, Adjusted R-squared:  0.1278 
## F-statistic: 1.879 on 1 and 5 DF,  p-value: 0.2287
m3 <- lm(potassium~poly(arid.gradient,2), data=nutrient.mean)
summary(m3)
## 
## Call:
## lm(formula = potassium ~ poly(arid.gradient, 2), data = nutrient.mean)
## 
## Residuals:
##       1       2       3       4       5       6       7 
##  -3.551 -65.927  32.611  57.492 -25.220  52.524 -47.931 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)   
## (Intercept)               186.56      22.69   8.221  0.00119 **
## poly(arid.gradient, 2)1   189.58      60.04   3.157  0.03427 * 
## poly(arid.gradient, 2)2   190.31      60.04   3.170  0.03387 * 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 60.04 on 4 degrees of freedom
## Multiple R-squared:  0.8334, Adjusted R-squared:  0.7502 
## F-statistic: 10.01 on 2 and 4 DF,  p-value: 0.02774

annual plant community repsonse

spp.data  <- read.csv("Data/ERG.communitydata.csv")
spp.data[is.na(spp.data)] <- 0

mean.spp <- spp.data %>% group_by(Year,Site, Microsite) %>%  summarize(abd=mean(Abundance), richness=mean(Richness), biomass=mean(Biomass))
mean.spp <- data.frame(mean.spp)

## collect aridity gradient data
site.climate <- rbind(s1.mean,s2.mean)
site.climate[,"Year"] <- c(rep("2016",7),rep("2017",7))

## combine climate data with community data
mean.spp <- merge(mean.spp, site.climate, by.y=c("Year","Site"))

# ## community responses season1
# mean.spp2016 <- subset(mean.spp, Year==2016)
# 
# ## richness
# plot(mean.spp2016[mean.spp2016$Microsite=="open","arid.gradient"], mean.spp2016[mean.spp2016$Microsite=="open","richness"], ylab="average site richness", xlab="aridity gradient")
# points(mean.spp2016[mean.spp2016$Microsite=="shrub","arid.gradient"],mean.spp2016[mean.spp2016$Microsite=="shrub","richness"], pch=19) 
# 
# m1 <- lm(richness~arid.gradient*Microsite, data=mean.spp2016)
# summary(m1)
# 
# ## abundance
# plot(mean.spp2016[mean.spp2016$Microsite=="open","arid.gradient"], mean.spp2016[mean.spp2016$Microsite=="open","abd"], ylab="average site abundance", xlab="aridity gradient")
# points(mean.spp2016[mean.spp2016$Microsite=="shrub","arid.gradient"],mean.spp2016[mean.spp2016$Microsite=="shrub","abd"], pch=19) 
# 
# m2 <- lm(abd~arid.gradient*Microsite, data=mean.spp2016)
# summary(m2)
# 
# ## biomass
# plot(mean.spp2016[mean.spp2016$Microsite=="open","arid.gradient"], mean.spp2016[mean.spp2016$Microsite=="open","biomass"], ylab="average site biomass", xlab="aridity gradient")
# points(mean.spp2016[mean.spp2016$Microsite=="shrub","arid.gradient"],mean.spp2016[mean.spp2016$Microsite=="shrub","biomass"], pch=19) 
# 
# m3 <- lm(biomass~arid.gradient*Microsite, data=mean.spp2016)
# summary(m3)
# 
# ##season2
# 
# ## community responses season2
# 
# mean.spp2017 <- subset(mean.spp, Year==2017)
# 
# 
# ## richness
# plot(mean.spp2017[mean.spp2017$Microsite=="open","arid.gradient"], mean.spp2017[mean.spp2017$Microsite=="open","richness"])
# points(mean.spp2017[mean.spp2017$Microsite=="shrub","arid.gradient"],mean.spp2017[mean.spp2017$Microsite=="shrub","richness"], pch=19) 
# 
# m1 <- lm(richness~arid.gradient*Microsite, data=mean.spp2017)
# summary(m1)
# 
# ## abundance
# plot(mean.spp2017[mean.spp2017$Microsite=="open","arid.gradient"], mean.spp2017[mean.spp2017$Microsite=="open","abd"])
# points(mean.spp2017[mean.spp2017$Microsite=="shrub","arid.gradient"],mean.spp2017[mean.spp2017$Microsite=="shrub","abd"], pch=19) 
# 
# m2 <- lm(abd~arid.gradient*Microsite, data=mean.spp2017)
# summary(m2)
# 
# ## biomass
# plot(mean.spp2017[mean.spp2017$Microsite=="open","arid.gradient"], mean.spp2017[mean.spp2017$Microsite=="open","biomass"])
# points(mean.spp2017[mean.spp2017$Microsite=="shrub","arid.gradient"],mean.spp2017[mean.spp2017$Microsite=="shrub","biomass"], pch=19) 
# 
# m3 <- lm(biomass~arid.gradient*Microsite, data=mean.spp2017)
# summary(m3)


## both seasons

## richness
plot(mean.spp[mean.spp$Microsite=="open","arid.gradient"], mean.spp[mean.spp$Microsite=="open","richness"],  ylab="average site richness", xlab="aridity gradient")
points(mean.spp[mean.spp$Microsite=="shrub","arid.gradient"],mean.spp[mean.spp$Microsite=="shrub","richness"], pch=19) 

m1 <- lm(richness~arid.gradient*Microsite, data=mean.spp)
summary(m1)
## 
## Call:
## lm(formula = richness ~ arid.gradient * Microsite, data = mean.spp)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.56169 -0.66225 -0.07639  0.66154  1.76433 
## 
## Coefficients:
##                              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                    4.9206     0.8381   5.871 4.68e-06 ***
## arid.gradient                  0.8699     0.2730   3.186  0.00397 ** 
## Micrositeshrub                -1.8968     1.1852  -1.600  0.12260    
## arid.gradient:Micrositeshrub  -0.5531     0.3861  -1.432  0.16490    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9061 on 24 degrees of freedom
## Multiple R-squared:  0.3356, Adjusted R-squared:  0.2526 
## F-statistic: 4.042 on 3 and 24 DF,  p-value: 0.01849
m1 <- lm(richness~poly(arid.gradient,2), data=mean.spp)
summary(m1)
## 
## Call:
## lm(formula = richness ~ poly(arid.gradient, 2), data = mean.spp)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.09400 -0.58831 -0.06033  0.47242  1.63726 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)    
## (Intercept)               2.2286     0.1418  15.719 1.81e-14 ***
## poly(arid.gradient, 2)1   2.7847     0.7502   3.712 0.001034 ** 
## poly(arid.gradient, 2)2  -2.7989     0.7502  -3.731 0.000986 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.7502 on 25 degrees of freedom
## Multiple R-squared:  0.5256, Adjusted R-squared:  0.4876 
## F-statistic: 13.85 on 2 and 25 DF,  p-value: 8.954e-05
ggplot(mean.spp) + geom_jitter(aes(x=arid.gradient, y=richness, fill=Microsite, color=Microsite), size=3) + 
 scale_color_manual(values=c("#E69F00","#56B4E9")) +  stat_smooth(method="lm", formula= y~poly(x,2),aes(x=arid.gradient, y=richness), color="black")+ ylab("Species richness") + xlab("Aridity Gradient")+ theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),axis.line = element_line(colour = "black"),panel.background = element_blank(),axis.text=element_text(size=14),axis.title=element_text(size=16)) 

## abundance
plot(mean.spp[mean.spp$Microsite=="open","arid.gradient"], mean.spp[mean.spp$Microsite=="open","abd"], ylab="average site abundance", xlab="aridity gradient")
points(mean.spp[mean.spp$Microsite=="shrub","arid.gradient"],mean.spp[mean.spp$Microsite=="shrub","abd"], pch=19) 

m2 <- lm(abd~arid.gradient*Microsite, data=mean.spp)
summary(m2)
## 
## Call:
## lm(formula = abd ~ arid.gradient * Microsite, data = mean.spp)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -64.997 -26.611  -5.081  25.895 108.541 
## 
## Coefficients:
##                              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                    162.65      38.28   4.249  0.00028 ***
## arid.gradient                   41.28      12.47   3.310  0.00294 ** 
## Micrositeshrub                  39.22      54.14   0.724  0.47582    
## arid.gradient:Micrositeshrub    10.04      17.64   0.569  0.57457    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 41.39 on 24 degrees of freedom
## Multiple R-squared:  0.5409, Adjusted R-squared:  0.4835 
## F-statistic: 9.426 on 3 and 24 DF,  p-value: 0.000268
m2 <- lm(ihs(abd)~arid.gradient, data=mean.spp)
summary(m2)
## 
## Call:
## lm(formula = ihs(abd) ~ arid.gradient, data = mean.spp)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.18927 -0.43160 -0.06177  0.41813  1.50236 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     8.6323     0.4214   20.48  < 2e-16 ***
## arid.gradient   1.7161     0.1373   12.50 1.69e-12 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.6444 on 26 degrees of freedom
## Multiple R-squared:  0.8573, Adjusted R-squared:  0.8519 
## F-statistic: 156.3 on 1 and 26 DF,  p-value: 1.687e-12
ggplot(mean.spp) + geom_jitter(aes(x=arid.gradient, y=ihs(abd), fill=Microsite, color=Microsite), size=3)+ 
 scale_color_manual(values=c("#E69F00","#56B4E9"))  +  stat_smooth(method="lm", formula= y~x,aes(x=arid.gradient, y=ihs(abd)), color="black")+ ylab("Annual abundance") + xlab("aridity gradient")+ theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),axis.line = element_line(colour = "black"),panel.background = element_blank(),axis.text=element_text(size=14),axis.title=element_text(size=16)) 

## biomass
plot(mean.spp[mean.spp$Microsite=="open","arid.gradient"], ihs(mean.spp[mean.spp$Microsite=="open","biomass"]), ylab="average site biomass", xlab="aridity gradient")
points(mean.spp[mean.spp$Microsite=="shrub","arid.gradient"],ihs(mean.spp[mean.spp$Microsite=="shrub","biomass"]), pch=19)

m3 <- lm(ihs(biomass)~arid.gradient*Microsite, data=mean.spp)
summary(m3)
## 
## Call:
## lm(formula = ihs(biomass) ~ arid.gradient * Microsite, data = mean.spp)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -0.6596 -0.5085 -0.0684  0.3314  1.2077 
## 
## Coefficients:
##                              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                    2.6251     0.5577   4.707 8.75e-05 ***
## arid.gradient                  0.5873     0.1817   3.233  0.00355 ** 
## Micrositeshrub                 1.6603     0.7888   2.105  0.04596 *  
## arid.gradient:Micrositeshrub   0.3698     0.2569   1.439  0.16303    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.603 on 24 degrees of freedom
## Multiple R-squared:  0.6498, Adjusted R-squared:  0.606 
## F-statistic: 14.84 on 3 and 24 DF,  p-value: 1.128e-05
ggplot(mean.spp) + geom_jitter(aes(x=arid.gradient, y=ihs(biomass), fill=Microsite, color=Microsite), size=3)+ 
 scale_color_manual(values=c("#E69F00","#56B4E9"))+  stat_smooth(method="lm", formula= y~x,aes(x=arid.gradient, y=ihs(biomass)), color="#56B4E9", data=subset(mean.spp, Microsite=="shrub"))  +  stat_smooth(method="lm", formula= y~x,aes(x=arid.gradient, y=ihs(biomass)), data=subset(mean.spp, Microsite=="open"), color="#E69F00")+ ylab("Annual biomass") + xlab("aridity gradient")+ theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),axis.line = element_line(colour = "black"),panel.background = element_blank(),axis.text=element_text(size=14),axis.title=element_text(size=16)) 

RDA community and environmental data

community <- read.csv("Data/ERG.communitydata.csv")
community[is.na(community)] <- 0

##2016
## add enviro data to dataframe
comm2016 <- subset(community, Year==2016)
site.vars2016[,"Site"] <- as.factor(row.names(site.vars2016))
comm2016 <- merge(comm2016, site.vars2016, by="Site")

## transform spp data
community.trans <- decostand(comm2016[,13:53], "hellinger")

rda1 <- rda(community.trans, comm2016[,54:57])
(R2adj <- RsquareAdj(rda1)$adj.r.squared) ## 50.4% of variation explained
## [1] 0.3871872
## build byplot manually
par(mar=c(4.5,4.5,0.5,0.5))
plot(rda1, type="n", xlim=c(-1,1), ylim=c(-1.5,1.5))

## calculate priority
spp.priority <- colSums(comm2016[,13:53])

## plot RDA1
colvec <- rep(c("blue","dodgerblue3","cyan","yellow2","orange","tomato","red2"),each=60)
points(rda1, display = "sites", col = "black", pch = c(21), bg = colvec)
orditorp(rda1, display = "species", cex = 0.7, col = "darkred", priority=spp.priority, air=0.5)
text(rda1,  display = "bp", col = "blue", cex = 0.8) 
legend("bottomright", pch=c(21), legend=unique(comm2016$Site), pt.bg=unique(colvec), cex=1)

## ordikplot to adjust species locations

## 2017
## add enviro data to dataframe
comm2017 <- subset(community, Year==2017)
site.vars[,"Site"] <- as.factor(row.names(site.vars))
comm2017 <- merge(comm2017, site.vars, by="Site")
# 
# ## transform spp data
# community.trans <- decostand(comm2017[,13:53], "hellinger")
# 
# rda2 <- rda(community.trans, comm2017[,54:58])
# (R2adj <- RsquareAdj(rda2)$adj.r.squared) ## 50.4% of variation explained
# 
# 
# ## build byplot manually
# par(mar=c(4.5,4.5,0.5,0.5))
# plot(rda2, type="n", xlim=c(-1,1), ylim=c(-1.5,1.5))
# 
# ## calculate priority
# spp.priority <- colSums(comm2017[,13:53])
# 
# ## plot RDA2
# colvec <- rep(c("blue","dodgerblue3","cyan","yellow2","orange","tomato","red2"),each=60)
# points(rda2, display = "sites", col = "black", pch = c(21), bg = colvec)
# orditorp(rda2, display = "species", cex = 0.7, col = "darkred", priority=spp.priority, air=0.5)
# text(rda2,  display = "bp", col = "blue", cex = 0.8) 
# legend("bottomright", pch=c(21), legend=unique(comm2016$Site), pt.bg=unique(colvec), cex=1)
# ## ordikplot to adjust species locations

Phytometer analysis

census <- read.csv("Data/ERG.phytometer.census.csv")
census[is.na(census)] <- 0
census[,"phyto.abd"] <- rowSums(census[,c("Phacelia","Plantago","Salvia")])

## calculate the number of seeds per gram
seed.mass <- read.csv("Data/seed.mass.csv")
seed.mass.avg <- seed.mass %>% group_by(species) %>%  summarize(avg.seed.gram=mean(seed.number),se.seed.gram=se(seed.number))
seed.mass.avg <- data.frame(seed.mass.avg)
seed.mass.avg
##      species avg.seed.gram se.seed.gram
## 1   Amsinkia         235.2     7.611833
## 2 Caulanthus        3019.0     8.916277
## 3  Lipidium          752.4     4.261455
## 4 Monolopia          737.8     7.831986
## 5   Phacelia         772.0     5.300943
## 6   Plantago         558.2     6.865858
## 7     Salvia         980.0     9.612492
## get proportion of seed germinated per 0.3 grams of seed
census[,"Phacelia.prop"] <- census[,"Phacelia"]/(seed.mass.avg$avg.seed.gram[which(seed.mass.avg$species=="Phacelia")]*0.3)
census[,"Plantago.prop"] <- census[,"Plantago"]/(seed.mass.avg$avg.seed.gram[which(seed.mass.avg$species=="Plantago")]*0.3)
census[,"Salvia.prop"] <- census[,"Salvia"]/(seed.mass.avg$avg.seed.gram[which(seed.mass.avg$species=="Salvia")]*0.3)


## beginning
census.int <- subset(census, Census=="emergence")

## all species
m1 <- glm.nb(Phacelia~ Microsite * Nutrient * Site + Year, data=census.int)
anova(m1, test="Chisq") ## Site * Micro and Year significant
## Analysis of Deviance Table
## 
## Model: Negative Binomial(0.8108), link: log
## 
## Response: Phacelia
## 
## Terms added sequentially (first to last)
## 
## 
##                         Df Deviance Resid. Df Resid. Dev  Pr(>Chi)    
## NULL                                      839    1079.71              
## Microsite                1   10.304       838    1069.41  0.001328 ** 
## Nutrient                 1    1.234       837    1068.17  0.266538    
## Site                     6  159.651       831     908.52 < 2.2e-16 ***
## Year                     1   31.424       830     877.10 2.073e-08 ***
## Microsite:Nutrient       1    0.046       829     877.05  0.829874    
## Microsite:Site           6   21.032       823     856.02  0.001811 ** 
## Nutrient:Site            6    5.542       817     850.48  0.476389    
## Microsite:Nutrient:Site  6    8.030       811     842.45  0.235879    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Phacelia
m1 <- glm.nb(Phacelia~ Microsite * Nutrient * Site + Year, data=census.int)
anova(m1, test="Chisq") ## Site * Micro and Year significant
## Analysis of Deviance Table
## 
## Model: Negative Binomial(0.8108), link: log
## 
## Response: Phacelia
## 
## Terms added sequentially (first to last)
## 
## 
##                         Df Deviance Resid. Df Resid. Dev  Pr(>Chi)    
## NULL                                      839    1079.71              
## Microsite                1   10.304       838    1069.41  0.001328 ** 
## Nutrient                 1    1.234       837    1068.17  0.266538    
## Site                     6  159.651       831     908.52 < 2.2e-16 ***
## Year                     1   31.424       830     877.10 2.073e-08 ***
## Microsite:Nutrient       1    0.046       829     877.05  0.829874    
## Microsite:Site           6   21.032       823     856.02  0.001811 ** 
## Nutrient:Site            6    5.542       817     850.48  0.476389    
## Microsite:Nutrient:Site  6    8.030       811     842.45  0.235879    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Plantago
m2 <- glm.nb(Plantago~ Microsite * Nutrient * Site + Year, data=census.int)
anova(m2, test="Chisq") ## Site * Micro and Year significant
## Analysis of Deviance Table
## 
## Model: Negative Binomial(0.5335), link: log
## 
## Response: Plantago
## 
## Terms added sequentially (first to last)
## 
## 
##                         Df Deviance Resid. Df Resid. Dev  Pr(>Chi)    
## NULL                                      839     984.30              
## Microsite                1    6.256       838     978.04   0.01238 *  
## Nutrient                 1    1.060       837     976.98   0.30332    
## Site                     6  162.687       831     814.30 < 2.2e-16 ***
## Year                     1    4.138       830     810.16   0.04194 *  
## Microsite:Nutrient       1    2.388       829     807.77   0.12224    
## Microsite:Site           6   43.000       823     764.77 1.167e-07 ***
## Nutrient:Site            6    6.391       817     758.38   0.38088    
## Microsite:Nutrient:Site  6    4.615       811     753.77   0.59403    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Salvia
m3 <- glm.nb(Salvia~ Microsite * Nutrient * Site + Year, data=census.int)
anova(m3, test="Chisq") ## Site * Micro and Year significant + (micro x nutrient)
## Analysis of Deviance Table
## 
## Model: Negative Binomial(0.8273), link: log
## 
## Response: Salvia
## 
## Terms added sequentially (first to last)
## 
## 
##                         Df Deviance Resid. Df Resid. Dev  Pr(>Chi)    
## NULL                                      839    1210.25              
## Microsite                1    5.924       838    1204.33 0.0149386 *  
## Nutrient                 1    1.513       837    1202.82 0.2186678    
## Site                     6  211.625       831     991.19 < 2.2e-16 ***
## Year                     1   17.463       830     973.73 2.929e-05 ***
## Microsite:Nutrient       1    7.824       829     965.90 0.0051548 ** 
## Microsite:Site           6   26.421       823     939.48 0.0001858 ***
## Nutrient:Site            6    4.587       817     934.90 0.5977624    
## Microsite:Nutrient:Site  6    1.566       811     933.33 0.9549811    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
census.int.plot <- gather(census.int, species, abundance, Phacelia:Salvia)

##calculate confidence interval
census.plot<- census.int.plot %>%  group_by(Microsite, species, Site, Year) %>%  summarize(avg=mean(abundance),ci=se(abundance)*1.96)

ggplot(census.plot, aes(x=Microsite, y=avg, fill=species))+
  geom_bar(position=position_dodge(), stat="identity")+
   geom_errorbar(aes(ymin=avg-ci, ymax=avg+ci),
                  width=.2,                    # Width of the error bars
                  position=position_dodge(.9))+ scale_fill_brewer() +ylab("Abundance")+ theme_Publication() + facet_grid(Year~Site) 

## end season
census.end <- subset(census, Census=="end")
census.end[,"Year"] <- as.factor(census.end$Year)

## run full model for phyto biomass
m1 <- aov(log(phyto.biomass)~ Site * Microsite * Nutrient + Year, data=subset(census.end, phyto.biomass>0))
summary(m1)
##                          Df Sum Sq Mean Sq F value   Pr(>F)    
## Site                      6  330.8   55.13  32.164  < 2e-16 ***
## Microsite                 1   10.9   10.88   6.347 0.012127 *  
## Nutrient                  1   30.3   30.25  17.651 3.24e-05 ***
## Year                      1  161.2  161.21  94.051  < 2e-16 ***
## Site:Microsite            6   23.9    3.99   2.327 0.031940 *  
## Site:Nutrient             6   41.9    6.98   4.073 0.000551 ***
## Microsite:Nutrient        1    1.7    1.73   1.011 0.315196    
## Site:Microsite:Nutrient   6    7.9    1.32   0.768 0.595068    
## Residuals               424  726.7    1.71                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(m1, "Microsite")
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = log(phyto.biomass) ~ Site * Microsite * Nutrient + Year, data = subset(census.end, phyto.biomass > 0))
## 
## $Microsite
##                 diff        lwr       upr     p adj
## shrub-open 0.3096905 0.06750868 0.5518722 0.0123243
## all species end of season
m2 <- glm.nb(phyto.abd~ Microsite * Nutrient * Site + Year, data=census.end)
anova(m2, test="Chisq") ## Site and microsite*nutrient significant
## Analysis of Deviance Table
## 
## Model: Negative Binomial(0.455), link: log
## 
## Response: phyto.abd
## 
## Terms added sequentially (first to last)
## 
## 
##                         Df Deviance Resid. Df Resid. Dev Pr(>Chi)    
## NULL                                      839    1009.51             
## Microsite                1    1.059       838    1008.45  0.30341    
## Nutrient                 1    2.527       837    1005.92  0.11194    
## Site                     6  135.420       831     870.50  < 2e-16 ***
## Year                     1    3.412       830     867.09  0.06474 .  
## Microsite:Nutrient       1    4.586       829     862.50  0.03224 *  
## Microsite:Site           6    5.340       823     857.16  0.50104    
## Nutrient:Site            6    5.169       817     852.00  0.52238    
## Microsite:Nutrient:Site  6    5.683       811     846.31  0.45956    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## run full model for phacelia biomass
m3 <- aov(log(Phacelia.biomass)~ Site * Microsite * Nutrient + Year, data=subset(census.end, Phacelia.biomass>0))
summary(m3) ## site and microsite
##                          Df Sum Sq Mean Sq F value   Pr(>F)    
## Site                      6   89.7   14.95   8.274 4.95e-08 ***
## Microsite                 1   21.2   21.20  11.738  0.00074 ***
## Nutrient                  1   10.2   10.19   5.642  0.01845 *  
## Year                      1   95.5   95.49  52.857 7.42e-12 ***
## Site:Microsite            6   23.6    3.93   2.173  0.04692 *  
## Site:Nutrient             6    8.0    1.34   0.742  0.61612    
## Microsite:Nutrient        1    0.0    0.00   0.001  0.97552    
## Site:Microsite:Nutrient   5    4.1    0.82   0.452  0.81122    
## Residuals               205  370.3    1.81                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(m3, "Microsite")
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = log(Phacelia.biomass) ~ Site * Microsite * Nutrient + Year, data = subset(census.end, Phacelia.biomass > 0))
## 
## $Microsite
##                 diff       lwr       upr     p adj
## shrub-open 0.6312547 0.2667053 0.9958041 0.0007714
## run full model for plantago biomass
m4 <- aov(log(Plantago.biomass)~ Site * Microsite * Nutrient + Year, data=subset(census.end, Plantago.biomass>0))
summary(m4) ## site and year + sitexmicrosite
##                          Df Sum Sq Mean Sq F value   Pr(>F)    
## Site                      6 116.35   19.39  19.843 2.32e-16 ***
## Microsite                 1   1.96    1.96   2.001    0.160    
## Nutrient                  1  32.03   32.03  32.778 6.80e-08 ***
## Year                      1 138.38  138.38 141.597  < 2e-16 ***
## Site:Microsite            6   4.87    0.81   0.830    0.549    
## Site:Nutrient             4   1.99    0.50   0.508    0.730    
## Microsite:Nutrient        1   3.17    3.17   3.244    0.074 .  
## Site:Microsite:Nutrient   3   0.65    0.22   0.220    0.882    
## Residuals               130 127.05    0.98                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(m4, "Microsite")
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = log(Plantago.biomass) ~ Site * Microsite * Nutrient + Year, data = subset(census.end, Plantago.biomass > 0))
## 
## $Microsite
##                  diff        lwr       upr     p adj
## shrub-open -0.2179629 -0.5348829 0.0989571 0.1759823
## run full model for plantago biomass
m5 <- aov(log(Salvia.biomass)~ Site * Microsite * Nutrient + Year, data=subset(census.end, Salvia.biomass>0))
summary(m5) ## site and year + sitexmicrosite
##                          Df Sum Sq Mean Sq F value   Pr(>F)    
## Site                      6  170.1   28.35  19.982  < 2e-16 ***
## Microsite                 1    0.0    0.01   0.008 0.929221    
## Nutrient                  1   21.4   21.43  15.103 0.000124 ***
## Year                      1   44.1   44.05  31.045 5.39e-08 ***
## Site:Microsite            6    6.8    1.14   0.802 0.568988    
## Site:Nutrient             6   46.2    7.70   5.424 2.35e-05 ***
## Microsite:Nutrient        1    0.5    0.55   0.387 0.534459    
## Site:Microsite:Nutrient   6   12.9    2.16   1.520 0.171069    
## Residuals               317  449.8    1.42                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(m5, "Microsite")
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = log(Salvia.biomass) ~ Site * Microsite * Nutrient + Year, data = subset(census.end, Salvia.biomass > 0))
## 
## $Microsite
##                  diff        lwr       upr     p adj
## shrub-open 0.01133515 -0.2406604 0.2633307 0.9295351
## run full model for phacelia abundance
m6 <- glm.nb(Phacelia.biomass~ Site * Microsite * Nutrient + Year, data=subset(census.end, Phacelia>0))
summary(m3) ## site and microsite
##                          Df Sum Sq Mean Sq F value   Pr(>F)    
## Site                      6   89.7   14.95   8.274 4.95e-08 ***
## Microsite                 1   21.2   21.20  11.738  0.00074 ***
## Nutrient                  1   10.2   10.19   5.642  0.01845 *  
## Year                      1   95.5   95.49  52.857 7.42e-12 ***
## Site:Microsite            6   23.6    3.93   2.173  0.04692 *  
## Site:Nutrient             6    8.0    1.34   0.742  0.61612    
## Microsite:Nutrient        1    0.0    0.00   0.001  0.97552    
## Site:Microsite:Nutrient   5    4.1    0.82   0.452  0.81122    
## Residuals               205  370.3    1.81                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(m3, "Microsite")
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = log(Phacelia.biomass) ~ Site * Microsite * Nutrient + Year, data = subset(census.end, Phacelia.biomass > 0))
## 
## $Microsite
##                 diff       lwr       upr     p adj
## shrub-open 0.6312547 0.2667053 0.9958041 0.0007714
## run full model for plantago biomass
m7 <- aov(log(Plantago)~ Site * Microsite * Nutrient + Year, data=subset(census.end, Plantago>0))
summary(m4) ## site and year + sitexmicrosite
##                          Df Sum Sq Mean Sq F value   Pr(>F)    
## Site                      6 116.35   19.39  19.843 2.32e-16 ***
## Microsite                 1   1.96    1.96   2.001    0.160    
## Nutrient                  1  32.03   32.03  32.778 6.80e-08 ***
## Year                      1 138.38  138.38 141.597  < 2e-16 ***
## Site:Microsite            6   4.87    0.81   0.830    0.549    
## Site:Nutrient             4   1.99    0.50   0.508    0.730    
## Microsite:Nutrient        1   3.17    3.17   3.244    0.074 .  
## Site:Microsite:Nutrient   3   0.65    0.22   0.220    0.882    
## Residuals               130 127.05    0.98                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(m4, "Microsite")
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = log(Plantago.biomass) ~ Site * Microsite * Nutrient + Year, data = subset(census.end, Plantago.biomass > 0))
## 
## $Microsite
##                  diff        lwr       upr     p adj
## shrub-open -0.2179629 -0.5348829 0.0989571 0.1759823
## run full model for plantago biomass
m8 <- aov(log(Salvia)~ Site * Microsite * Nutrient + Year, data=subset(census.end, Salvia>0))
summary(m5) ## site and year + sitexmicrosite
##                          Df Sum Sq Mean Sq F value   Pr(>F)    
## Site                      6  170.1   28.35  19.982  < 2e-16 ***
## Microsite                 1    0.0    0.01   0.008 0.929221    
## Nutrient                  1   21.4   21.43  15.103 0.000124 ***
## Year                      1   44.1   44.05  31.045 5.39e-08 ***
## Site:Microsite            6    6.8    1.14   0.802 0.568988    
## Site:Nutrient             6   46.2    7.70   5.424 2.35e-05 ***
## Microsite:Nutrient        1    0.5    0.55   0.387 0.534459    
## Site:Microsite:Nutrient   6   12.9    2.16   1.520 0.171069    
## Residuals               317  449.8    1.42                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(m5, "Microsite")
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = log(Salvia.biomass) ~ Site * Microsite * Nutrient + Year, data = subset(census.end, Salvia.biomass > 0))
## 
## $Microsite
##                  diff        lwr       upr     p adj
## shrub-open 0.01133515 -0.2406604 0.2633307 0.9295351
census.end.plot <- gather(census.end, species, biomass, Phacelia.biomass:Salvia.biomass)

##calculate confidence interval
census.plot<- census.end.plot %>%  group_by(Microsite, species, Site, Year) %>%  summarize(avg=mean(biomass),ci=se(biomass)*1.96)

ggplot(census.plot, aes(x=Microsite, y=avg, fill=species))+
  geom_bar(position=position_dodge(), stat="identity")+
   geom_errorbar(aes(ymin=avg-ci, ymax=avg+ci),
                  width=.2,                    # Width of the error bars
                  position=position_dodge(.9))+ scale_fill_brewer() +ylab("Abundance")+ theme_Publication() + facet_grid(Year~Site) 

### RDA
# 
# ##collect environmental variables for site
# nutrients <- read.csv("Data/ERG.soilnutrients.csv")
# nutrients.mean <- nutrients %>% group_by(gradient,site, microsite) %>% summarise_each(funs(mean))
# nutrients.vars <- data.frame(nutrients.mean)
# 
# ## minimum explanation of variables
# ambient2016[is.na(ambient2016)] <- 0
# community <- ambient2016 %>% group_by(Gradient,Site, Microsite) %>% summarise_each(funs(sum))
# community <- data.frame(community)
# 
# end.census <- subset(census2016, census=="end")
# end.census  <- end.census  %>% group_by(Gradient,Site, Microsite) %>% summarise_each(funs(mean))
# end.census<- data.frame(end.census)
# 
# ## daily means
# day.mean <- aggregate(HOBO.data, by=list(day=HOBO.data$Day, micro=HOBO.data$Microsite, site=HOBO.data$Site), mean)
# 
# ## summarize daily means across sites
# means <- day.mean %>% group_by(gradient, micro) %>% summarize(temp=mean(Temp),rh=mean(RH),temp.se=se(Temp),rh.se=se(RH))
# means <- data.frame(means)
# 
# 
# envs <- data.frame(swc=end.census[,"swc"],nutrients.vars[,c("N","P","K")], means[,c("temp","rh")])
# 
# ## hellinger transformation
# community.trans <- decostand(community[,12:42], "hellinger")
# 
# ## rda with environmental variables
# rda1 <- rda(community.trans, envs)
# anova(rda1)
# (R2adj <- RsquareAdj(rda1)$adj.r.squared) ## 25.7% of variation explained
# 
# 
# ## build byplot manually
# par(mar=c(4.5,4.5,0.5,0.5))
# plot(rda1, type="n", xlim=c(-1,1))
# 
# with(community, levels(Site))
# 
# spp.priority <- colSums(community[,12:42])
# 
# colvec <- c("blue","dodgerblue3","cyan","yellow2","orange","tomato","red2")
# with(community, points(rda1, display = "sites", col = "black", pch = c(21,22), bg = colvec[Site]))
# orditorp(rda1, display = "species", cex = 0.7, col = "darkred", priority=spp.priority, air=1,)
# text(rda1,  display = "bp", col = "blue", cex = 0.8) 
# legend("bottomright", pch=c(21), legend=community$Site[community$Microsite=="shrub"], pt.bg=colvec, cex=1)
# 
# ## check variable explanation
# vif.cca(rda1)
# 
# v.clim <- cbind(envs[,c("temp","rh")])
# v.nut <- cbind(envs[,c("N","P","K")])
# v.swc <- envs[,"swc"]
# 
# x <- varpart(community.trans,v.clim,v.nut,v.swc)
# 
# showvarparts(3)

Difference in effect size

library(bootES)
## Loading required package: boot
## 
## Attaching package: 'boot'
## The following object is masked from 'package:lattice':
## 
##     melanoma
effect.cal <- function(x){ bootES(x, R=999, data.col="Biomass", group.col="Microsite", contrast=c(shrub=1,open=-1), effect.type=c("cohens.d"))}

effect.site <- by(subset(comm2016, Site != "Barstow"), comm2016$Site[comm2016$Site != "Barstow"], FUN=effect.cal)

site.summary <- rbind(summary(effect.site$Panoche),summary(effect.site$Cuyama),summary(effect.site$TejonRanch),data.frame(stat=0,ci.low=0,ci.high=0,bias=0,std.error=0),summary(effect.site$MojavePreserve),summary(effect.site$SheepholeValley),summary(effect.site$Tecopa)) 
site.summary[5,] <- 0
site.summary <- sapply(site.summary, as.numeric)

par(mar=c(4.5,4.5,.5,.5))
plot(c(1:7),site.summary[,"stat"], xlim=c(0.5,7.5), ylim=c(-1,3), pch=19, cex=1.4, ylab="Hedge's G", xlab="", xaxt="n")
arrows(c(1:7),as.numeric(site.summary[,"ci.high"]),c(1:7),site.summary[,"ci.low"], angle=90, code=3, length=0, lwd=2)
abline(h=0, lty=2, lwd=2)
axis(1, c(1:7), labels=site.names, cex.axis=1)

## phytometer

effect.cal <- function(x){ bootES(x, R=999, data.col="phyto.biomass", group.col="Microsite", contrast=c(shrub=1,open=-1), effect.type=c("cohens.d"))}

census2016 <- subset(census, Census=="end" & Year ==2016)

effect.site <- by(subset(census2016, Site != "Barstow"), census2016$Site[census2016$Site != "Barstow"], FUN=effect.cal)

site.summary <- rbind(summary(effect.site$Panoche),summary(effect.site$Cuyama),summary(effect.site$TejonRanch),data.frame(stat=0,ci.low=0,ci.high=0,bias=0,std.error=0),summary(effect.site$MojavePreserve),summary(effect.site$SheepholeValley),summary(effect.site$Tecopa)) 
site.summary <- sapply(site.summary, as.numeric)

par(mar=c(4.5,4.5,.5,.5))
plot(c(1:7),site.summary[,"stat"], xlim=c(0.5,7.5), ylim=c(-1,3), pch=19, cex=1.4, ylab="Hedge's G", xlab="", xaxt="n")
arrows(c(1:7),site.summary[,"ci.high"],c(1:7),site.summary[,"ci.low"], angle=90, code=3, length=0, lwd=2)
abline(h=0, lty=2, lwd=2)
axis(1, c(1:7), labels=site.names, cex.axis=1)

Rii between years

rii.dat <- rii(community, j=1:8, var=c("Abundance","Richness","Biomass"))

rii.mean <- rii.dat %>% group_by(Year, Gradient, Site) %>% summarize(avg=mean(Biomass),se=se(Biomass))
rii.mean <- data.frame(rii.mean)

plot(rii.mean[rii.mean$Year==2016,"Gradient"]-.1,rii.mean[rii.mean$Year==2016,"avg"], ylim=c(-1,1), pch=19, cex=1.5, xlim=c(0.5,7.5), ylab="Rii Biomass", xlab="Site", xaxt="n", cex.lab=1.5)
axis(1, 1:7, lab=unique(rii.mean$Site))
error.bar(rii.mean[rii.mean$Year==2016,"Gradient"]-.1,rii.mean[rii.mean$Year==2016,"avg"],rii.mean[rii.mean$Year==2016,"se"])
error.bar(rii.mean[rii.mean$Year==2017,"Gradient"]+.1,rii.mean[rii.mean$Year==2017,"avg"],rii.mean[rii.mean$Year==2017,"se"])
points(rii.mean[rii.mean$Year==2017,"Gradient"]+.1,rii.mean[rii.mean$Year==2017,"avg"], pch=21, bg="Grey50", cex=1.5)
abline(h=0, lwd=2, lty=2)
legend(6.3,-0.55, c("2016","2017"), pch=22, pt.bg=c("Black","Grey50"), cex=1.6)

## add year column to precipitation data
precip[,"Year"] <- ifelse(precip$season=="season.1","2016","2017")
rii.precip <- merge(precip,rii.mean, by=c("Year","Gradient"))

Native non-native comparisons

## load species list
spp.list <- read.csv("Data/ERG.specieslist.csv")

## convert data to long format
status <- gather(community, species, abundance, 13:53)
names(spp.list)[1] <- "species" ## rename species column for merge

## combine native-non-native status with data set
status <- merge(status, spp.list, by="species")

## summarize per plot native and non-natives, convert back to wide format
mean.status <- status %>% group_by(Year, Site, Microsite, Rep, status) %>% summarize(abundance=sum(abundance))
mean.status <- spread(mean.status, status, abundance)

## calculate site means for native and non-native
mean.status <- mean.status %>% group_by(Year, Site, Microsite) %>% summarize(native=mean(native), non.native=mean(non.native))

## merge with original community data
mean.spp <- merge(mean.spp, mean.status, by=c("Year","Site","Microsite"))

## native
m1 <- lm(ihs(native) ~ poly(arid.gradient,2), data=subset(mean.spp, Microsite=="shrub"))
summary(m1)
## 
## Call:
## lm(formula = ihs(native) ~ poly(arid.gradient, 2), data = subset(mean.spp, 
##     Microsite == "shrub"))
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.03332 -0.65782 -0.04555  0.60465  1.25478 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)    
## (Intercept)               1.6533     0.2185   7.565 1.11e-05 ***
## poly(arid.gradient, 2)1   0.3447     0.8177   0.422  0.68145    
## poly(arid.gradient, 2)2  -2.9468     0.8177  -3.604  0.00414 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.8177 on 11 degrees of freedom
## Multiple R-squared:  0.5448, Adjusted R-squared:  0.4621 
## F-statistic: 6.583 on 2 and 11 DF,  p-value: 0.01318
m1 <- lm(ihs(native) ~ arid.gradient, data=subset(mean.spp, Microsite=="open"))
summary(m1)
## 
## Call:
## lm(formula = ihs(native) ~ arid.gradient, data = subset(mean.spp, 
##     Microsite == "open"))
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.89497 -0.73527  0.02181  0.50785  1.81716 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)   
## (Intercept)     4.0097     1.0111   3.966  0.00187 **
## arid.gradient   0.6575     0.3294   1.996  0.06913 . 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.093 on 12 degrees of freedom
## Multiple R-squared:  0.2493, Adjusted R-squared:  0.1867 
## F-statistic: 3.984 on 1 and 12 DF,  p-value: 0.06913
m1 <- lm(ihs(native) ~ poly(arid.gradient,2)*Microsite, data=mean.spp)
summary(m1)
## 
## Call:
## lm(formula = ihs(native) ~ poly(arid.gradient, 2) * Microsite, 
##     data = mean.spp)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.6483 -0.6647 -0.1444  0.5383  2.0863 
## 
## Coefficients:
##                                        Estimate Std. Error t value
## (Intercept)                              2.0776     0.2616   7.943
## poly(arid.gradient, 2)1                  3.0858     1.3841   2.229
## poly(arid.gradient, 2)2                 -1.1145     1.3841  -0.805
## Micrositeshrub                          -0.4243     0.3699  -1.147
## poly(arid.gradient, 2)1:Micrositeshrub  -2.5983     1.9574  -1.327
## poly(arid.gradient, 2)2:Micrositeshrub  -3.0529     1.9574  -1.560
##                                        Pr(>|t|)    
## (Intercept)                            6.65e-08 ***
## poly(arid.gradient, 2)1                  0.0363 *  
## poly(arid.gradient, 2)2                  0.4293    
## Micrositeshrub                           0.2637    
## poly(arid.gradient, 2)1:Micrositeshrub   0.1980    
## poly(arid.gradient, 2)2:Micrositeshrub   0.1331    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9787 on 22 degrees of freedom
## Multiple R-squared:  0.4229, Adjusted R-squared:  0.2918 
## F-statistic: 3.225 on 5 and 22 DF,  p-value: 0.0247
ggplot(mean.spp) + geom_jitter(aes(x=arid.gradient, y=ihs(native), fill=Microsite, color=Microsite), size=3)+ 
 scale_color_manual(values=c("#E69F00","#56B4E9"))+  stat_smooth(method="lm", formula= y~poly(x,2),aes(x=arid.gradient, y=ihs(native)), color="black")+ ylab("Native abundance") + xlab("aridity gradient")+ theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),axis.line = element_line(colour = "black"),panel.background = element_blank(),axis.text=element_text(size=14),axis.title=element_text(size=16)) 

## non-native
m1 <- lm(ihs(non.native) ~ poly(arid.gradient,2), data=subset(mean.spp, Microsite=="shrub"))
summary(m1)
## 
## Call:
## lm(formula = ihs(non.native) ~ poly(arid.gradient, 2), data = subset(mean.spp, 
##     Microsite == "shrub"))
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -2.32477 -0.20348 -0.05303  0.53297  2.27555 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)    
## (Intercept)               3.0777     0.3340   9.214 1.66e-06 ***
## poly(arid.gradient, 2)1   6.5874     1.2498   5.271 0.000264 ***
## poly(arid.gradient, 2)2   0.7721     1.2498   0.618 0.549292    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.25 on 11 degrees of freedom
## Multiple R-squared:  0.7191, Adjusted R-squared:  0.668 
## F-statistic: 14.08 on 2 and 11 DF,  p-value: 0.0009268
m1 <- lm(ihs(non.native) ~ poly(arid.gradient,2), data=subset(mean.spp, Microsite=="open"))
summary(m1)
## 
## Call:
## lm(formula = ihs(non.native) ~ poly(arid.gradient, 2), data = subset(mean.spp, 
##     Microsite == "open"))
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.66301 -0.60211  0.01432  0.55858  1.91804 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)    
## (Intercept)               2.8844     0.2828  10.200 6.06e-07 ***
## poly(arid.gradient, 2)1   6.1497     1.0581   5.812 0.000117 ***
## poly(arid.gradient, 2)2   0.4615     1.0581   0.436 0.671141    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.058 on 11 degrees of freedom
## Multiple R-squared:  0.7554, Adjusted R-squared:  0.7109 
## F-statistic: 16.98 on 2 and 11 DF,  p-value: 0.0004331
m1 <- lm(ihs(non.native) ~ poly(arid.gradient,2)*Microsite, data=mean.spp)
summary(m1)
## 
## Call:
## lm(formula = ihs(non.native) ~ poly(arid.gradient, 2) * Microsite, 
##     data = mean.spp)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -2.32477 -0.43001 -0.01113  0.58589  2.27555 
## 
## Coefficients:
##                                        Estimate Std. Error t value
## (Intercept)                              2.8844     0.3095   9.320
## poly(arid.gradient, 2)1                  8.6970     1.6376   5.311
## poly(arid.gradient, 2)2                  0.6527     1.6376   0.399
## Micrositeshrub                           0.1933     0.4377   0.442
## poly(arid.gradient, 2)1:Micrositeshrub   0.6190     2.3159   0.267
## poly(arid.gradient, 2)2:Micrositeshrub   0.4393     2.3159   0.190
##                                        Pr(>|t|)    
## (Intercept)                            4.28e-09 ***
## poly(arid.gradient, 2)1                2.49e-05 ***
## poly(arid.gradient, 2)2                   0.694    
## Micrositeshrub                            0.663    
## poly(arid.gradient, 2)1:Micrositeshrub    0.792    
## poly(arid.gradient, 2)2:Micrositeshrub    0.851    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.158 on 22 degrees of freedom
## Multiple R-squared:  0.7361, Adjusted R-squared:  0.6761 
## F-statistic: 12.27 on 5 and 22 DF,  p-value: 9.2e-06
m1 <- lm(ihs(non.native) ~ arid.gradient*Microsite, data=mean.spp)
summary(m1)
## 
## Call:
## lm(formula = ihs(non.native) ~ arid.gradient * Microsite, data = mean.spp)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.5300 -0.4742  0.1206  0.7847  2.0747 
## 
## Coefficients:
##                              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                    8.3299     1.0394   8.014 3.06e-08 ***
## arid.gradient                  1.8530     0.3386   5.472 1.26e-05 ***
## Micrositeshrub                 0.5808     1.4699   0.395    0.696    
## arid.gradient:Micrositeshrub   0.1319     0.4789   0.275    0.785    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.124 on 24 degrees of freedom
## Multiple R-squared:  0.7289, Adjusted R-squared:  0.695 
## F-statistic: 21.51 on 3 and 24 DF,  p-value: 5.506e-07
ggplot(mean.spp) + geom_jitter(aes(x=arid.gradient, y=ihs(non.native), fill=Microsite, color=Microsite), size=3)+ 
 scale_color_manual(values=c("#E69F00","#56B4E9"))+  stat_smooth(method="lm", formula= y~x,aes(x=arid.gradient, y=ihs(non.native)), color="black")+ ylab("Non-native abundance") + xlab("aridity gradient")+ theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),axis.line = element_line(colour = "black"),panel.background = element_blank(),axis.text=element_text(size=14),axis.title=element_text(size=16)) 

Phylogenetics

library(picante)
library(ape)
library(brranching)
library(taxize)

## load species list to create tree
spp.list <- read.csv("Data/ERG.specieslist.csv")

## create a combined species name column and drop erinoginum because not to species level
taxa <- paste(spp.list$Genus, spp.list$Species.name)
taxa <- taxa[taxa!="Erinoginum spp"]

## create tree of phylogeny
tree <- phylomatic(taxa=taxa, get = 'POST')

## calculate branch lengths using  Grafen's method
## Grafen, A. (1989) The phylogenetic regression. Philosophical Transactions of the Royal society of London. Series B. Biological Sciences, 326, 119–157.
tree2 <- compute.brlen(tree, method="Grafen")

## view tree and outputs to verify branches
plot(tree2)

tree2
## 
## Phylogenetic tree with 39 tips and 29 internal nodes.
## 
## Tip labels:
##  cryptantha_barbigera, cryptantha_intermedia, phacelia_crenulata, phacelia_tanacetifolia, pectocarya_spp, amsinckia_grandiflora, ...
## Node labels:
##  poales_to_asterales, eudicots, , , asterids, ericales_to_asterales, ...
## 
## Rooted; includes branch lengths.
## format community to only have site and microsite
comm <- community %>% group_by(Year, Site, Microsite) %>% summarise_if(is.numeric, funs(sum(., na.rm=T)))
comm <- comm[,c(1:3,13:53)] ## extract species abundances only
comm <- data.frame(comm) ## convert to dataframe
comm[,"site.micro"] <- paste(comm$Site,comm$Microsite,as.character(comm$Year)) ## create site by microsite column
rownames(comm) <- comm[,length(comm)] ## replace row names with site by microsite
comm[,c("Year","Site","Microsite","Erinoginum.spp","Boraginaceae.sp.","site.micro")] <- NULL ## drop all columns but ones for analysis

## match species name format
spp.list[,"spp.name"] <- paste(spp.list$Genus, spp.list$Species.name)
colnames(comm) <- spp.list$spp.name[match(colnames(comm), spp.list$Species.shorthand)]

## match species format
names(comm) <- gsub(" ", "_", names(comm), fixed=T) ## underscores instead of spaces
names(comm) <- gsub("__", "_", names(comm), fixed=T) ## replace double underscores with one
names(comm) <- tolower(names(comm)) ## lower case names


## mean phylogenetic distance
mpd.data <- mpd(comm, cophenetic(tree2), abundance.weighted=T)

## format dataframe
mpd.data <- data.frame(Year=c(rep(2016,14),rep(2017,14)),Microsite=c("open","shrub"),site=rownames(comm), mpd=mpd.data)
mpd.data[,"Site"] <- gsub(" open", "", mpd.data[,"site"]) 
mpd.data[,"Site"] <- gsub(" shrub", "", mpd.data[,"Site"]) 
mpd.data[,"Site"] <- gsub(" 2016", "", mpd.data[,"Site"]) 
mpd.data[,"Site"] <- gsub(" 2017", "", mpd.data[,"Site"]) 
mpd.data[,"site"] <- NULL
mpd.data[is.na(mpd.data)] <- 0 ## barstow no plants
 

## getspecies richness for sites
spp.rich <- community %>% group_by(Year, Site, Microsite) %>% summarize(richness=mean(Richness))
spp.rich <- data.frame(spp.rich)

## join data
mpd.rich <- merge(mpd.data, spp.rich, by=c("Site","Microsite","Year"))

mpd.arid <- merge(mpd.rich, mean.spp, by=c("Site","Microsite","Year"))


## phylogenetic distance
mpd.arid <- mpd.arid[c(2,4:nrow(mpd.arid)),] ## drop barstow

m1 <- lm(mpd~arid.gradient*Microsite, data=mpd.arid)
summary(m1)
## 
## Call:
## lm(formula = mpd ~ arid.gradient * Microsite, data = mpd.arid)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.51050 -0.19590  0.06299  0.14972  0.71607 
## 
## Coefficients:
##                              Estimate Std. Error t value Pr(>|t|)   
## (Intercept)                   0.97618    0.30321   3.219  0.00395 **
## arid.gradient                 0.04716    0.10394   0.454  0.65451   
## Micrositeshrub               -1.07891    0.42881  -2.516  0.01966 * 
## arid.gradient:Micrositeshrub -0.30441    0.14700  -2.071  0.05031 . 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.2938 on 22 degrees of freedom
## Multiple R-squared:  0.3146, Adjusted R-squared:  0.2211 
## F-statistic: 3.365 on 3 and 22 DF,  p-value: 0.03692
ggplot(mpd.arid) + geom_jitter(aes(x=arid.gradient, y=mpd, fill=Microsite, color=Microsite), size=3) + 
 scale_color_manual(values=c("#E69F00","#56B4E9")) +  stat_smooth(method="lm", formula= y~x,aes(x=arid.gradient, y=mpd), data=subset(mpd.arid, Microsite=="shrub"), color="#56B4E9")+  stat_smooth(method="lm", formula= y~x,aes(x=arid.gradient, y=mpd), data=subset(mpd.arid, Microsite=="open"), color="#E69F00")+ ylab("Phylogenetic Community Dissimilarity") + xlab("Aridity Gradient")+ theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),axis.line = element_line(colour = "black"),panel.background = element_blank(),axis.text=element_text(size=14),axis.title=element_text(size=16)) 

m1 <- lm(mpd~arid.gradient, data=subset(mpd.arid, Microsite=="shrub"))
summary(m1)
## 
## Call:
## lm(formula = mpd ~ arid.gradient, data = subset(mpd.arid, Microsite == 
##     "shrub"))
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.51050 -0.23112  0.03664  0.14204  0.71607 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)  
## (Intercept)    -0.1027     0.3563  -0.288    0.778  
## arid.gradient  -0.2573     0.1222  -2.106    0.059 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.3452 on 11 degrees of freedom
## Multiple R-squared:  0.2873, Adjusted R-squared:  0.2225 
## F-statistic: 4.435 on 1 and 11 DF,  p-value: 0.05899
m2 <- lm(mpd~arid.gradient, data=subset(mpd.arid, Microsite=="open"))
summary(m2)
## 
## Call:
## lm(formula = mpd ~ arid.gradient, data = subset(mpd.arid, Microsite == 
##     "open"))
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.38078 -0.09025  0.08952  0.15668  0.33783 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)   
## (Intercept)    0.97618    0.23852   4.093  0.00178 **
## arid.gradient  0.04716    0.08177   0.577  0.57575   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.2311 on 11 degrees of freedom
## Multiple R-squared:  0.02935,    Adjusted R-squared:  -0.05889 
## F-statistic: 0.3326 on 1 and 11 DF,  p-value: 0.5758

Changes in Mechanisms along gradient

### Nutrients 
nutrient.mean <- nutrients.climate %>% group_by(Site, arid.gradient, microsite) %>% summarize(nitrogen=mean(N), phosphorus=mean(P), potassium=mean(K))
nutrient.mean <- data.frame(nutrient.mean)

## nitrogen
m1 <- lm(nitrogen ~ arid.gradient, data=subset(nutrient.mean, microsite=="open"))
summary(m1)
## 
## Call:
## lm(formula = nitrogen ~ arid.gradient, data = subset(nutrient.mean, 
##     microsite == "open"))
## 
## Residuals:
##        1        3        5        7        9       11       13 
##  1.62742  0.10479 -1.35216  0.97441 -0.05162 -0.76608 -0.53677 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)  
## (Intercept)    -2.7141     1.5041  -1.804   0.1310  
## arid.gradient  -1.2879     0.4313  -2.986   0.0306 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.124 on 5 degrees of freedom
## Multiple R-squared:  0.6407, Adjusted R-squared:  0.5689 
## F-statistic: 8.917 on 1 and 5 DF,  p-value: 0.03058
anova(m1)
## Analysis of Variance Table
## 
## Response: nitrogen
##               Df Sum Sq Mean Sq F value  Pr(>F)  
## arid.gradient  1 11.263  11.263  8.9174 0.03058 *
## Residuals      5  6.315   1.263                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
ggplot(nutrient.mean) + geom_jitter(aes(x=arid.gradient, y=nitrogen, fill=microsite, color=microsite), size=3)+ 
 scale_color_manual(values=c("#E69F00","#56B4E9")) +  
  stat_smooth(method="lm", formula= y~x,aes(x=arid.gradient, y=nitrogen), color="#56B4E9", data=subset(nutrient.mean, microsite=="shrub")) +  
  stat_smooth(method="lm", formula= y~x,aes(x=arid.gradient, y=nitrogen), color="#E69F00", data=subset(nutrient.mean, microsite=="open"))+ ylab("Nitrogen") + xlab("Aridity Gradient")+ theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),axis.line = element_line(colour = "black"),panel.background = element_blank(),axis.text=element_text(size=14),axis.title=element_text(size=16)) 

## phosphorus
m2 <- lm(phosphorus ~ arid.gradient * microsite, data=nutrient.mean)
summary(m2)
## 
## Call:
## lm(formula = phosphorus ~ arid.gradient * microsite, data = nutrient.mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -6.1684 -2.3472  0.6103  1.9735  7.0340 
## 
## Coefficients:
##                              Estimate Std. Error t value Pr(>|t|)
## (Intercept)                    8.8933     5.3802   1.653    0.129
## arid.gradient                  0.7555     1.5428   0.490    0.635
## micrositeshrub                10.5952     7.6088   1.392    0.194
## arid.gradient:micrositeshrub   2.0483     2.1819   0.939    0.370
## 
## Residual standard error: 4.02 on 10 degrees of freedom
## Multiple R-squared:  0.3967, Adjusted R-squared:  0.2158 
## F-statistic: 2.192 on 3 and 10 DF,  p-value: 0.152
anova(m2)
## Analysis of Variance Table
## 
## Response: phosphorus
##                         Df  Sum Sq Mean Sq F value Pr(>F)
## arid.gradient            1  43.011  43.011  2.6614 0.1339
## microsite                1  49.031  49.031  3.0339 0.1122
## arid.gradient:microsite  1  14.243  14.243  0.8813 0.3700
## Residuals               10 161.610  16.161
ggplot(nutrient.mean) + geom_jitter(aes(x=arid.gradient, y=phosphorus, fill=microsite, color=microsite), size=3)+ 
 scale_color_manual(values=c("#E69F00","#56B4E9"))  ## not significant

m3 <- lm(potassium ~ arid.gradient * microsite, data=nutrient.mean)
summary(m3)
## 
## Call:
## lm(formula = potassium ~ arid.gradient * microsite, data = nutrient.mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -160.67  -64.64  -14.22   64.63  206.92 
## 
## Coefficients:
##                              Estimate Std. Error t value Pr(>|t|)  
## (Intercept)                    290.78     145.57   1.998   0.0737 .
## arid.gradient                   44.64      41.74   1.069   0.3101  
## micrositeshrub                 278.33     205.87   1.352   0.2062  
## arid.gradient:micrositeshrub    56.24      59.03   0.953   0.3633  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 108.8 on 10 degrees of freedom
## Multiple R-squared:  0.4843, Adjusted R-squared:  0.3295 
## F-statistic:  3.13 on 3 and 10 DF,  p-value: 0.07444
anova(m3)
## Analysis of Variance Table
## 
## Response: potassium
##                         Df Sum Sq Mean Sq F value Pr(>F)  
## arid.gradient            1  71879   71879  6.0754 0.0334 *
## microsite                1  28476   28476  2.4069 0.1519  
## arid.gradient:microsite  1  10736   10736  0.9074 0.3633  
## Residuals               10 118313   11831                 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## potassium
ggplot(nutrient.mean) + geom_jitter(aes(x=arid.gradient, y=potassium, fill=microsite, color=microsite), size=3)+ 
 scale_color_manual(values=c("#E69F00","#56B4E9")) +  
  stat_smooth(method="lm", formula= y~x,aes(x=arid.gradient, y=potassium), color="black") 

#### Shrubs increase nitrogen availability at aridity extremes #### Shrubs do not effect potassium or phosphorus availability

se <- function(x) sd(na.omit(x))/sqrt(length(na.omit(x)))


smc <- census %>% group_by(Year, Site, Microsite, Census) %>% summarize(swc.avg=mean(swc), swc.se=se(swc))

smc.arid <- merge(smc, site.climate, by=c("Site","Year"))


## early swc
smc.early <- subset(smc.arid, Census=="emergence")

ggplot(smc.early)  + geom_jitter(aes(x=arid.gradient, y=swc.avg, fill=Microsite, color=Microsite), size=3)+ 
 scale_color_manual(values=c("#E69F00","#56B4E9"))+  
  stat_smooth(method="lm", formula= y~poly(x,2),aes(x=arid.gradient, y=swc.avg), color="black") + ylab("Percent soil Moisture Content (emergence)") + xlab("Aridity Gradient")+ theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),axis.line = element_line(colour = "black"),panel.background = element_blank(),axis.text=element_text(size=14),axis.title=element_text(size=16)) 

m1 <- lm(swc.avg~ poly(arid.gradient,2) * Microsite, data=smc.early)
summary(m1)
## 
## Call:
## lm(formula = swc.avg ~ poly(arid.gradient, 2) * Microsite, data = smc.early)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -8.8989 -2.2340  0.1864  2.2093  8.7293 
## 
## Coefficients:
##                                        Estimate Std. Error t value
## (Intercept)                             11.1650     1.3550   8.240
## poly(arid.gradient, 2)1                 32.2475     7.1700   4.498
## poly(arid.gradient, 2)2                 20.8675     7.1700   2.910
## Micrositeshrub                          -0.4679     1.9163  -0.244
## poly(arid.gradient, 2)1:Micrositeshrub  -3.3904    10.1399  -0.334
## poly(arid.gradient, 2)2:Micrositeshrub  -3.1002    10.1399  -0.306
##                                        Pr(>|t|)    
## (Intercept)                             3.6e-08 ***
## poly(arid.gradient, 2)1                0.000179 ***
## poly(arid.gradient, 2)2                0.008109 ** 
## Micrositeshrub                         0.809376    
## poly(arid.gradient, 2)1:Micrositeshrub 0.741276    
## poly(arid.gradient, 2)2:Micrositeshrub 0.762672    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 5.07 on 22 degrees of freedom
## Multiple R-squared:  0.699,  Adjusted R-squared:  0.6306 
## F-statistic: 10.22 on 5 and 22 DF,  p-value: 3.647e-05
## end swc
smc.end <- subset(smc.arid, Census=="end")

ggplot(smc.end)  + geom_jitter(aes(x=arid.gradient, y=swc.avg, fill=Microsite, color=Microsite), size=3)+ 
 scale_color_manual(values=c("#E69F00","#56B4E9"))+  
  stat_smooth(method="lm", formula= y~poly(x,2),aes(x=arid.gradient, y=swc.avg), color="black") + ylab("Percent soil Moisture Content (end)") + xlab("Aridity Gradient")+ theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),axis.line = element_line(colour = "black"),panel.background = element_blank(),axis.text=element_text(size=14),axis.title=element_text(size=16)) 

m1 <- lm(swc.avg~ poly(arid.gradient,2) * Microsite, data=smc.end)
summary(m1)
## 
## Call:
## lm(formula = swc.avg ~ poly(arid.gradient, 2) * Microsite, data = smc.end)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -8.7611 -2.4087  0.0009  2.0666  9.3760 
## 
## Coefficients:
##                                        Estimate Std. Error t value
## (Intercept)                               5.093      1.335   3.815
## poly(arid.gradient, 2)1                  15.502      7.065   2.194
## poly(arid.gradient, 2)2                  17.571      7.065   2.487
## Micrositeshrub                           -0.576      1.888  -0.305
## poly(arid.gradient, 2)1:Micrositeshrub   -1.945      9.991  -0.195
## poly(arid.gradient, 2)2:Micrositeshrub   -3.453      9.991  -0.346
##                                        Pr(>|t|)    
## (Intercept)                            0.000946 ***
## poly(arid.gradient, 2)1                0.039073 *  
## poly(arid.gradient, 2)2                0.020948 *  
## Micrositeshrub                         0.763211    
## poly(arid.gradient, 2)1:Micrositeshrub 0.847426    
## poly(arid.gradient, 2)2:Micrositeshrub 0.732914    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4.996 on 22 degrees of freedom
## Multiple R-squared:  0.4604, Adjusted R-squared:  0.3377 
## F-statistic: 3.754 on 5 and 22 DF,  p-value: 0.01312

Shrubs do not alter soil moisture availability

hobo.means <- HOBO.data %>% group_by(season, Microsite, Site) %>% summarize(temp.avg=mean(Temp),temp.var=var(Temp),temp.se=se(Temp),rh.avg=mean(RH, na.rm=T),rh.var=var(RH, na.rm=T),rh.se=se(RH),frost=(sum(na.omit(Temp)<0)/length(na.omit(Temp))*100),heat=(sum(na.omit(Temp)>30)/length(na.omit(Temp))*100))
hobo.means[,"Year"] <- c(rep(2016,14),rep(2017,14))


hobo.arid <- merge(hobo.means, site.climate, by=c("Site","Year"))

## temperature
ggplot(hobo.arid)  + geom_jitter(aes(x=arid.gradient, y=temp.avg, fill=Microsite, color=Microsite), size=3)+ 
 scale_color_manual(values=c("#E69F00","#56B4E9"))+  
  stat_smooth(method="lm", formula= y~poly(x,2),aes(x=arid.gradient, y=temp.avg), color="black") + ylab("Average Temperature (C°)") + xlab("Aridity Gradient")+ theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),axis.line = element_line(colour = "black"),panel.background = element_blank(),axis.text=element_text(size=14),axis.title=element_text(size=16)) 

m1 <- lm(temp.avg~arid.gradient * Microsite, data=hobo.arid)
summary(m1)
## 
## Call:
## lm(formula = temp.avg ~ arid.gradient * Microsite, data = hobo.arid)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.2362 -0.7204 -0.3884  0.4197  3.2981 
## 
## Coefficients:
##                              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                   6.21950    1.38277   4.498 0.000149 ***
## arid.gradient                -1.75909    0.45045  -3.905 0.000669 ***
## Micrositeshrub               -1.42763    1.95554  -0.730 0.472428    
## arid.gradient:Micrositeshrub  0.00911    0.63704   0.014 0.988709    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.495 on 24 degrees of freedom
## Multiple R-squared:  0.6064, Adjusted R-squared:  0.5571 
## F-statistic: 12.32 on 3 and 24 DF,  p-value: 4.451e-05
anova(m1)
## Analysis of Variance Table
## 
## Response: temp.avg
##                         Df Sum Sq Mean Sq F value    Pr(>F)    
## arid.gradient            1 67.815  67.815 30.3425 1.154e-05 ***
## Microsite                1 14.807  14.807  6.6252   0.01666 *  
## arid.gradient:Microsite  1  0.000   0.000  0.0002   0.98871    
## Residuals               24 53.639   2.235                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
m1.s <- lm(temp.avg~arid.gradient, data=subset(hobo.arid, Microsite=="shrub"))
summary(m1.s)
## 
## Call:
## lm(formula = temp.avg ~ arid.gradient, data = subset(hobo.arid, 
##     Microsite == "shrub"))
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.5836 -0.9458 -0.4008  0.4122  3.2981 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)   
## (Intercept)     4.7919     1.3727   3.491  0.00446 **
## arid.gradient  -1.7500     0.4472  -3.913  0.00206 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.484 on 12 degrees of freedom
## Multiple R-squared:  0.5607, Adjusted R-squared:  0.5241 
## F-statistic: 15.31 on 1 and 12 DF,  p-value: 0.00206
## RH
ggplot(hobo.arid)  + geom_jitter(aes(x=arid.gradient, y=rh.avg, fill=Microsite, color=Microsite), size=3)+ 
 scale_color_manual(values=c("#E69F00","#56B4E9"))+  
  stat_smooth(method="lm", formula= y~exp(x),aes(x=arid.gradient, y=rh.avg), color="black") + ylab("Relative Humidity (%)") + xlab("Aridity Gradient")+ theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),axis.line = element_line(colour = "black"),panel.background = element_blank(),axis.text=element_text(size=14),axis.title=element_text(size=16)) 

m2 <- lm(log(rh.avg)~arid.gradient * Microsite, data=hobo.arid)
summary(m2)
## 
## Call:
## lm(formula = log(rh.avg) ~ arid.gradient * Microsite, data = hobo.arid)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.313369 -0.094086  0.008993  0.111107  0.252701 
## 
## Coefficients:
##                              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                  4.703640   0.165465  28.427  < 2e-16 ***
## arid.gradient                0.251051   0.053902   4.658 9.92e-05 ***
## Micrositeshrub               0.049072   0.234003   0.210    0.836    
## arid.gradient:Micrositeshrub 0.004987   0.076229   0.065    0.948    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.1789 on 24 degrees of freedom
## Multiple R-squared:  0.6497, Adjusted R-squared:  0.6059 
## F-statistic: 14.84 on 3 and 24 DF,  p-value: 1.131e-05
anova(m2)
## Analysis of Variance Table
## 
## Response: log(rh.avg)
##                         Df  Sum Sq Mean Sq F value    Pr(>F)    
## arid.gradient            1 1.41614 1.41614 44.2510 7.014e-07 ***
## Microsite                1 0.00829 0.00829  0.2591    0.6154    
## arid.gradient:Microsite  1 0.00014 0.00014  0.0043    0.9484    
## Residuals               24 0.76806 0.03200                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## frost
hobo.arid2 <- subset(hobo.arid, Site!="Cuyama") ## drop cuyama because abnormal frost periods

ggplot(hobo.arid2)  + geom_jitter(aes(x=arid.gradient, y=frost, fill=Microsite, color=Microsite), size=3)+ 
 scale_color_manual(values=c("#E69F00","#56B4E9"))+  
  stat_smooth(method="lm", formula= y~x,aes(x=arid.gradient, y=frost), color="black") + ylab("Number of frost days") + xlab("Aridity Gradient")+ theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),axis.line = element_line(colour = "black"),panel.background = element_blank(),axis.text=element_text(size=14),axis.title=element_text(size=16)) 

m3 <- lm(frost~arid.gradient * Microsite, data=hobo.arid2)
summary(m3)
## 
## Call:
## lm(formula = frost ~ arid.gradient * Microsite, data = hobo.arid2)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.2695 -1.3394 -0.3817  1.9954  3.3744 
## 
## Coefficients:
##                              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                   10.7896     2.2613   4.771 0.000116 ***
## arid.gradient                  1.4999     0.7049   2.128 0.045970 *  
## Micrositeshrub                -2.3006     3.1980  -0.719 0.480218    
## arid.gradient:Micrositeshrub  -0.3756     0.9969  -0.377 0.710319    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.117 on 20 degrees of freedom
## Multiple R-squared:  0.3059, Adjusted R-squared:  0.2018 
## F-statistic: 2.938 on 3 and 20 DF,  p-value: 0.05818
anova(m3)
## Analysis of Variance Table
## 
## Response: frost
##                         Df Sum Sq Mean Sq F value  Pr(>F)  
## arid.gradient            1 31.066 31.0655  6.9304 0.01596 *
## Microsite                1  7.806  7.8058  1.7414 0.20187  
## arid.gradient:Microsite  1  0.636  0.6363  0.1419 0.71032  
## Residuals               20 89.649  4.4825                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## temperature variability

ggplot(hobo.arid)  + geom_jitter(aes(x=arid.gradient, y=temp.var.x, fill=Microsite, color=Microsite), size=3)+ 
 scale_color_manual(values=c("#E69F00","#56B4E9"))+  
  stat_smooth(method="lm", formula= y~exp(x),aes(x=arid.gradient, y=temp.var.x), color="#E69F00", data=subset(hobo.arid, Microsite=="open")) + ylab("Number of frost days")+  
  stat_smooth(method="lm", formula= y~exp(x),aes(x=arid.gradient, y=temp.var.x), color="#56B4E9", data=subset(hobo.arid, Microsite=="shrub")) + ylab("temperature variation") + xlab("Aridity Gradient")+ theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),axis.line = element_line(colour = "black"),panel.background = element_blank(),axis.text=element_text(size=14),axis.title=element_text(size=16)) 

m4 <- lm(log(temp.var.x)~arid.gradient * Microsite, data=hobo.arid)
summary(m4)
## 
## Call:
## lm(formula = log(temp.var.x) ~ arid.gradient * Microsite, data = hobo.arid)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.75540 -0.15564  0.02433  0.17182  0.46013 
## 
## Coefficients:
##                              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                   3.83222    0.26061  14.705 1.67e-13 ***
## arid.gradient                -0.19133    0.08490  -2.254   0.0336 *  
## Micrositeshrub               -0.46416    0.36856  -1.259   0.2200    
## arid.gradient:Micrositeshrub -0.02535    0.12006  -0.211   0.8346    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.2818 on 24 degrees of freedom
## Multiple R-squared:   0.51,  Adjusted R-squared:  0.4488 
## F-statistic: 8.327 on 3 and 24 DF,  p-value: 0.0005707
anova(m4)
## Analysis of Variance Table
## 
## Response: log(temp.var.x)
##                         Df  Sum Sq Mean Sq F value   Pr(>F)   
## arid.gradient            1 0.91677 0.91677 11.5480 0.002368 **
## Microsite                1 1.06294 1.06294 13.3892 0.001241 **
## arid.gradient:Microsite  1 0.00354 0.00354  0.0446 0.834588   
## Residuals               24 1.90531 0.07939                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##  extreme heat
## reverse inverse hyperbolic sine
hs <- function(x) {
    y <- 0.5*exp(-x)*(exp(2*x)-1)
    return(y)
}

ggplot(hobo.arid)  + geom_jitter(aes(x=arid.gradient, y=heat, fill=Microsite, color=Microsite), size=3)+ 
 scale_color_manual(values=c("#E69F00","#56B4E9"))+  
  stat_smooth(method="lm", formula= y~hs(x),aes(x=arid.gradient, y=heat), color="#E69F00", data=subset(hobo.arid, Microsite=="open")) +  
  stat_smooth(method="lm", formula= y~hs(x),aes(x=arid.gradient, y=heat), color="#56B4E9", data=subset(hobo.arid, Microsite=="shrub")) + ylab("extreme heat days variation") + xlab("Aridity Gradient")+ theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),axis.line = element_line(colour = "black"),panel.background = element_blank(),axis.text=element_text(size=14),axis.title=element_text(size=16)) 

m5 <- lm(ihs(heat)~arid.gradient * Microsite, data=hobo.arid)
summary(m5)
## 
## Call:
## lm(formula = ihs(heat) ~ arid.gradient * Microsite, data = hobo.arid)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -0.7532 -0.3832 -0.1102  0.2072  1.2432 
## 
## Coefficients:
##                              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                   -0.2199     0.4899  -0.449 0.657489    
## arid.gradient                 -0.7396     0.1596  -4.635 0.000105 ***
## Micrositeshrub                -0.1480     0.6928  -0.214 0.832615    
## arid.gradient:Micrositeshrub   0.1787     0.2257   0.792 0.436163    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.5296 on 24 degrees of freedom
## Multiple R-squared:  0.6529, Adjusted R-squared:  0.6095 
## F-statistic: 15.05 on 3 and 24 DF,  p-value: 1.015e-05
anova(m5)
## Analysis of Variance Table
## 
## Response: ihs(heat)
##                         Df Sum Sq Mean Sq F value    Pr(>F)    
## arid.gradient            1 9.3144  9.3144 33.2088 6.124e-06 ***
## Microsite                1 3.1726  3.1726 11.3114   0.00258 ** 
## arid.gradient:Microsite  1 0.1759  0.1759  0.6271   0.43616    
## Residuals               24 6.7315  0.2805                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

shrubs reduce temperature variability

shrubz <- read.csv("Data/ERG.shrub.csv")

shrub.mean <- shrubz %>% group_by(Site, Microsite) %>% summarize(compact=mean(Compaction))

shrub.clim <- merge(shrub.mean, site.climate, by=c("Site"))
#shrub.clim <- subset(shrub.clim, Year==2016)

ggplot(shrub.clim)  + geom_jitter(aes(x=arid.gradient, y=compact, fill=Microsite, color=Microsite), size=3)+ 
 scale_color_manual(values=c("#E69F00","#56B4E9"))+   stat_smooth(method="lm", formula= y~exp(x),aes(x=arid.gradient, y=compact), color="#E69F00", data=subset(shrub.clim, Microsite=="open"))+
  stat_smooth(method="lm", formula= y~exp(x),aes(x=arid.gradient, y=compact), color="#56B4E9", data=subset(shrub.clim, Microsite=="shrub")) + ylab("soil compaction") + xlab("Aridity Gradient")+ theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),axis.line = element_line(colour = "black"),panel.background = element_blank(),axis.text=element_text(size=14),axis.title=element_text(size=16)) 

m1 <- lm(log(compact) ~ arid.gradient* Microsite, data=shrub.clim)
summary(m1)
## 
## Call:
## lm(formula = log(compact) ~ arid.gradient * Microsite, data = shrub.clim)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.36122 -0.13659 -0.02823  0.05479  0.61368 
## 
## Coefficients:
##                              Estimate Std. Error t value Pr(>|t|)   
## (Intercept)                   0.26645    0.23301   1.144  0.26410   
## arid.gradient                -0.07328    0.07590  -0.965  0.34394   
## Micrositeshrub               -1.19983    0.32952  -3.641  0.00130 **
## arid.gradient:Micrositeshrub -0.32933    0.10734  -3.068  0.00528 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.2519 on 24 degrees of freedom
## Multiple R-squared:  0.5932, Adjusted R-squared:  0.5424 
## F-statistic: 11.67 on 3 and 24 DF,  p-value: 6.53e-05
anova(m1)
## Analysis of Variance Table
## 
## Response: log(compact)
##                         Df  Sum Sq Mean Sq F value    Pr(>F)    
## arid.gradient            1 1.24730 1.24730 19.6548 0.0001755 ***
## Microsite                1 0.37672 0.37672  5.9362 0.0226248 *  
## arid.gradient:Microsite  1 0.59733 0.59733  9.4127 0.0052769 ** 
## Residuals               24 1.52305 0.06346                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

shrubs reduce soil compaction and increase substrate penetration

Phytometer models

library(lsmeans)
## The 'lsmeans' package is being deprecated.
## Users are encouraged to switch to 'emmeans'.
## See help('transition') for more information, including how
## to convert 'lsmeans' objects and scripts to work with 'emmeans'.
census.arid <- merge(census.end, site.climate, by=c("Year","Site"))


## all species end of season
m1 <- glm.nb(phyto.abd~ Microsite * Nutrient * arid.gradient, data=census.arid)
anova(m2, test="Chisq") ## Site and microsite*nutrient significant
## Analysis of Variance Table
## 
## Response: log(rh.avg)
##                         Df  Sum Sq Mean Sq F value    Pr(>F)    
## arid.gradient            1 1.41614 1.41614 44.2510 7.014e-07 ***
## Microsite                1 0.00829 0.00829  0.2591    0.6154    
## arid.gradient:Microsite  1 0.00014 0.00014  0.0043    0.9484    
## Residuals               24 0.76806 0.03200                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## run full model for phacelia biomass
m1 <- aov(log(Phacelia.biomass)~ Microsite * Nutrient * arid.gradient,  data=subset(census.arid, Phacelia.biomass>0))
summary(m1) ## site and microsite
##                                   Df Sum Sq Mean Sq F value   Pr(>F)    
## Microsite                          1   19.0   19.00   9.126  0.00281 ** 
## Nutrient                           1    8.8    8.76   4.207  0.04141 *  
## arid.gradient                      1  122.7  122.66  58.927 4.95e-13 ***
## Microsite:Nutrient                 1    0.2    0.16   0.078  0.78078    
## Microsite:arid.gradient            1    1.4    1.41   0.678  0.41121    
## Nutrient:arid.gradient             1    0.0    0.00   0.000  0.99128    
## Microsite:Nutrient:arid.gradient   1    2.3    2.27   1.088  0.29795    
## Residuals                        225  468.3    2.08                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(m1, "Microsite")
## Warning in replications(paste("~", xx), data = mf): non-factors ignored:
## arid.gradient
## Warning in replications(paste("~", xx), data = mf): non-factors ignored:
## Microsite, arid.gradient
## Warning in replications(paste("~", xx), data = mf): non-factors ignored:
## Nutrient, arid.gradient
## Warning in replications(paste("~", xx), data = mf): non-factors ignored:
## Microsite, Nutrient, arid.gradient
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = log(Phacelia.biomass) ~ Microsite * Nutrient * arid.gradient, data = subset(census.arid, Phacelia.biomass > 0))
## 
## $Microsite
##                 diff       lwr       upr     p adj
## shrub-open 0.5995862 0.2084783 0.9906941 0.0028111
TukeyHSD(m1, "Nutrient")
## Warning in replications(paste("~", xx), data = mf): non-factors ignored:
## arid.gradient
## Warning in replications(paste("~", xx), data = mf): non-factors ignored:
## Microsite, arid.gradient
## Warning in replications(paste("~", xx), data = mf): non-factors ignored:
## Nutrient, arid.gradient
## Warning in replications(paste("~", xx), data = mf): non-factors ignored:
## Microsite, Nutrient, arid.gradient
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = log(Phacelia.biomass) ~ Microsite * Nutrient * arid.gradient, data = subset(census.arid, Phacelia.biomass > 0))
## 
## $Nutrient
##                     diff        lwr         upr     p adj
## Ambient-Added -0.3865601 -0.7590709 -0.01404918 0.0420304
## run full model for plantago biomass
m2 <- aov(log(Plantago.biomass)~ Microsite * Nutrient * arid.gradient,  data=subset(census.arid, Plantago.biomass>0))
summary(m2) ## site and year + sitexmicrosite
##                                   Df Sum Sq Mean Sq F value   Pr(>F)    
## Microsite                          1   9.59    9.59   5.718 0.018067 *  
## Nutrient                           1  26.14   26.14  15.582 0.000122 ***
## arid.gradient                      1 136.39  136.39  81.293 1.01e-15 ***
## Microsite:Nutrient                 1   0.61    0.61   0.366 0.546337    
## Microsite:arid.gradient            1   1.57    1.57   0.937 0.334540    
## Nutrient:arid.gradient             1   3.96    3.96   2.362 0.126491    
## Microsite:Nutrient:arid.gradient   1   3.21    3.21   1.912 0.168873    
## Residuals                        146 244.96    1.68                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(m2, "Microsite")
## Warning in replications(paste("~", xx), data = mf): non-factors ignored:
## arid.gradient
## Warning in replications(paste("~", xx), data = mf): non-factors ignored:
## Microsite, arid.gradient
## Warning in replications(paste("~", xx), data = mf): non-factors ignored:
## Nutrient, arid.gradient
## Warning in replications(paste("~", xx), data = mf): non-factors ignored:
## Microsite, Nutrient, arid.gradient
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = log(Plantago.biomass) ~ Microsite * Nutrient * arid.gradient, data = subset(census.arid, Plantago.biomass > 0))
## 
## $Microsite
##                  diff        lwr         upr     p adj
## shrub-open -0.5018942 -0.9167114 -0.08707699 0.0180668
TukeyHSD(m2, "Nutrient")
## Warning in replications(paste("~", xx), data = mf): non-factors ignored:
## arid.gradient
## Warning in replications(paste("~", xx), data = mf): non-factors ignored:
## Microsite, arid.gradient
## Warning in replications(paste("~", xx), data = mf): non-factors ignored:
## Nutrient, arid.gradient
## Warning in replications(paste("~", xx), data = mf): non-factors ignored:
## Microsite, Nutrient, arid.gradient
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = log(Plantago.biomass) ~ Microsite * Nutrient * arid.gradient, data = subset(census.arid, Plantago.biomass > 0))
## 
## $Nutrient
##                     diff      lwr        upr     p adj
## Ambient-Added -0.8205043 -1.23339 -0.4076185 0.0001319
## run full model for salvia biomass
m3 <- aov(log(Salvia.biomass)~ Microsite * Nutrient * arid.gradient,  data=subset(census.arid, Salvia.biomass>0))
summary(m3) ## site and year + sitexmicrosite
##                                   Df Sum Sq Mean Sq F value   Pr(>F)    
## Microsite                          1    0.1    0.11   0.054 0.815771    
## Nutrient                           1   24.8   24.84  12.494 0.000465 ***
## arid.gradient                      1   33.7   33.73  16.965 4.79e-05 ***
## Microsite:Nutrient                 1    0.2    0.23   0.115 0.734281    
## Microsite:arid.gradient            1    0.6    0.59   0.299 0.585029    
## Nutrient:arid.gradient             1   20.3   20.28  10.199 0.001538 ** 
## Microsite:Nutrient:arid.gradient   1    0.0    0.00   0.000 0.988929    
## Residuals                        338  672.1    1.99                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(m3, "Microsite")
## Warning in replications(paste("~", xx), data = mf): non-factors ignored:
## arid.gradient
## Warning in replications(paste("~", xx), data = mf): non-factors ignored:
## Microsite, arid.gradient
## Warning in replications(paste("~", xx), data = mf): non-factors ignored:
## Nutrient, arid.gradient
## Warning in replications(paste("~", xx), data = mf): non-factors ignored:
## Microsite, Nutrient, arid.gradient
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = log(Salvia.biomass) ~ Microsite * Nutrient * arid.gradient, data = subset(census.arid, Salvia.biomass > 0))
## 
## $Microsite
##                  diff      lwr       upr     p adj
## shrub-open 0.03535378 -0.26289 0.3335975 0.8157715
m3.n <- lm(log(Salvia.biomass)~ arid.gradient,  data=subset(census.arid, Salvia.biomass>0 & Nutrient =="Added"))
m3.a <- lm(log(Salvia.biomass)~ arid.gradient,  data=subset(census.arid, Salvia.biomass>0 & Nutrient =="Ambient"))

## abundance
## run full model for phacelia abundance
m1 <- glm.nb(Phacelia~ Microsite * Nutrient * arid.gradient,  data=census.arid)
summary(m1) ## site and microsite
## 
## Call:
## glm.nb(formula = Phacelia ~ Microsite * Nutrient * arid.gradient, 
##     data = census.arid, init.theta = 0.2308296117, link = log)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -1.03830  -0.81531  -0.76103   0.01823   3.03660  
## 
## Coefficients:
##                                                Estimate Std. Error z value
## (Intercept)                                  -0.0007756  0.5883363  -0.001
## Micrositeshrub                                1.2705913  0.8115605   1.566
## NutrientAmbient                               0.1077404  0.8333728   0.129
## arid.gradient                                 0.1858087  0.1944620   0.956
## Micrositeshrub:NutrientAmbient                0.1849576  1.1409892   0.162
## Micrositeshrub:arid.gradient                  0.2621592  0.2696848   0.972
## NutrientAmbient:arid.gradient                 0.0421013  0.2759568   0.153
## Micrositeshrub:NutrientAmbient:arid.gradient -0.0505405  0.3789622  -0.133
##                                              Pr(>|z|)
## (Intercept)                                     0.999
## Micrositeshrub                                  0.117
## NutrientAmbient                                 0.897
## arid.gradient                                   0.339
## Micrositeshrub:NutrientAmbient                  0.871
## Micrositeshrub:arid.gradient                    0.331
## NutrientAmbient:arid.gradient                   0.879
## Micrositeshrub:NutrientAmbient:arid.gradient    0.894
## 
## (Dispersion parameter for Negative Binomial(0.2308) family taken to be 1)
## 
##     Null deviance: 585.87  on 839  degrees of freedom
## Residual deviance: 550.93  on 832  degrees of freedom
## AIC: 1941.5
## 
## Number of Fisher Scoring iterations: 1
## 
## 
##               Theta:  0.2308 
##           Std. Err.:  0.0231 
## 
##  2 x log-likelihood:  -1923.4540
anova(m1, test="Chisq")
## Warning in anova.negbin(m1, test = "Chisq"): tests made without re-
## estimating 'theta'
## Analysis of Deviance Table
## 
## Model: Negative Binomial(0.2308), link: log
## 
## Response: Phacelia
## 
## Terms added sequentially (first to last)
## 
## 
##                                  Df Deviance Resid. Df Resid. Dev
## NULL                                               839     585.87
## Microsite                         1  19.9145       838     565.96
## Nutrient                          1   1.0729       837     564.89
## arid.gradient                     1  11.4485       836     553.44
## Microsite:Nutrient                1   1.0254       835     552.41
## Microsite:arid.gradient           1   1.4563       834     550.96
## Nutrient:arid.gradient            1   0.0064       833     550.95
## Microsite:Nutrient:arid.gradient  1   0.0167       832     550.93
##                                   Pr(>Chi)    
## NULL                                          
## Microsite                        8.098e-06 ***
## Nutrient                         0.3002852    
## arid.gradient                    0.0007155 ***
## Microsite:Nutrient               0.3112354    
## Microsite:arid.gradient          0.2275227    
## Nutrient:arid.gradient           0.9362121    
## Microsite:Nutrient:arid.gradient 0.8971935    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
lsmeans(m1, pairwise~"Microsite")
## NOTE: Results may be misleading due to involvement in interactions
## $lsmeans
##  Microsite     lsmean        SE df  asymp.LCL  asymp.UCL
##  open      -0.5548206 0.1205597 NA -0.7911133 -0.3185280
##  shrub      0.1120837 0.1124787 NA -0.1083705  0.3325379
## 
## Results are averaged over the levels of: Nutrient 
## Results are given on the log (not the response) scale. 
## Confidence level used: 0.95 
## 
## $contrasts
##  contrast       estimate        SE df z.ratio p.value
##  open - shrub -0.6669043 0.1648821 NA  -4.045  0.0001
## 
## Results are averaged over the levels of: Nutrient 
## Results are given on the log (not the response) scale.
## run full model for plantago biomass
m2 <- glm.nb(Plantago~ Microsite * Nutrient * arid.gradient,  data=census.arid)
summary(m2) ## site and microsite
## 
## Call:
## glm.nb(formula = Plantago ~ Microsite * Nutrient * arid.gradient, 
##     data = census.arid, init.theta = 0.1287536289, link = log)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -0.8053  -0.7211  -0.6578  -0.5689   2.4129  
## 
## Coefficients:
##                                                Estimate Std. Error z value
## (Intercept)                                   1.0031515  0.7094622   1.414
## Micrositeshrub                               -2.8208986  1.0387678  -2.716
## NutrientAmbient                              -0.0810403  1.0160835  -0.080
## arid.gradient                                 0.3420451  0.2337751   1.463
## Micrositeshrub:NutrientAmbient                0.5580731  1.4672180   0.380
## Micrositeshrub:arid.gradient                 -0.7058890  0.3368271  -2.096
## NutrientAmbient:arid.gradient                 0.0826214  0.3361349   0.246
## Micrositeshrub:NutrientAmbient:arid.gradient -0.0006522  0.4776644  -0.001
##                                              Pr(>|z|)   
## (Intercept)                                   0.15737   
## Micrositeshrub                                0.00662 **
## NutrientAmbient                               0.93643   
## arid.gradient                                 0.14343   
## Micrositeshrub:NutrientAmbient                0.70368   
## Micrositeshrub:arid.gradient                  0.03611 * 
## NutrientAmbient:arid.gradient                 0.80584   
## Micrositeshrub:NutrientAmbient:arid.gradient  0.99891   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Negative Binomial(0.1288) family taken to be 1)
## 
##     Null deviance: 427.38  on 839  degrees of freedom
## Residual deviance: 414.78  on 832  degrees of freedom
## AIC: 1596.1
## 
## Number of Fisher Scoring iterations: 1
## 
## 
##               Theta:  0.1288 
##           Std. Err.:  0.0138 
## 
##  2 x log-likelihood:  -1578.1100
anova(m2, test="Chisq")
## Warning in anova.negbin(m2, test = "Chisq"): tests made without re-
## estimating 'theta'
## Analysis of Deviance Table
## 
## Model: Negative Binomial(0.1288), link: log
## 
## Response: Plantago
## 
## Terms added sequentially (first to last)
## 
## 
##                                  Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL                                               839     427.38         
## Microsite                         1   5.5584       838     421.82  0.01839
## Nutrient                          1   0.0296       837     421.79  0.86334
## arid.gradient                     1   0.5610       836     421.23  0.45386
## Microsite:Nutrient                1   1.6621       835     419.56  0.19732
## Microsite:arid.gradient           1   4.7218       834     414.84  0.02978
## Nutrient:arid.gradient            1   0.0649       833     414.78  0.79889
## Microsite:Nutrient:arid.gradient  1   0.0000       832     414.78  0.99859
##                                   
## NULL                              
## Microsite                        *
## Nutrient                          
## arid.gradient                     
## Microsite:Nutrient                
## Microsite:arid.gradient          *
## Nutrient:arid.gradient            
## Microsite:Nutrient:arid.gradient  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
lsmeans(m2, pairwise~"Microsite")
## NOTE: Results may be misleading due to involvement in interactions
## $lsmeans
##  Microsite     lsmean        SE df  asymp.LCL  asymp.UCL
##  open      -0.1639688 0.1466943 NA -0.4514843  0.1235467
##  shrub     -0.6304166 0.1522519 NA -0.9288248 -0.3320084
## 
## Results are averaged over the levels of: Nutrient 
## Results are given on the log (not the response) scale. 
## Confidence level used: 0.95 
## 
## $contrasts
##  contrast      estimate        SE df z.ratio p.value
##  open - shrub 0.4664478 0.2114234 NA   2.206  0.0274
## 
## Results are averaged over the levels of: Nutrient 
## Results are given on the log (not the response) scale.
## run full model for salvia biomass
m3 <- glm.nb(Salvia~ Microsite * Nutrient * arid.gradient,  data=census.arid)
summary(m3) ## site and microsite
## 
## Call:
## glm.nb(formula = Salvia ~ Microsite * Nutrient * arid.gradient, 
##     data = census.arid, init.theta = 0.3061627656, link = log)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -1.3585  -1.1226  -0.8020   0.2021   2.2024  
## 
## Coefficients:
##                                              Estimate Std. Error z value
## (Intercept)                                   2.39712    0.46918   5.109
## Micrositeshrub                               -0.02997    0.67161  -0.045
## NutrientAmbient                               0.61208    0.67236   0.910
## arid.gradient                                 0.57763    0.15655   3.690
## Micrositeshrub:NutrientAmbient                0.16062    0.94881   0.169
## Micrositeshrub:arid.gradient                  0.07096    0.22516   0.315
## NutrientAmbient:arid.gradient                 0.24763    0.22629   1.094
## Micrositeshrub:NutrientAmbient:arid.gradient -0.14470    0.31899  -0.454
##                                              Pr(>|z|)    
## (Intercept)                                  3.24e-07 ***
## Micrositeshrub                               0.964406    
## NutrientAmbient                              0.362646    
## arid.gradient                                0.000225 ***
## Micrositeshrub:NutrientAmbient               0.865571    
## Micrositeshrub:arid.gradient                 0.752644    
## NutrientAmbient:arid.gradient                0.273802    
## Micrositeshrub:NutrientAmbient:arid.gradient 0.650108    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Negative Binomial(0.3062) family taken to be 1)
## 
##     Null deviance: 785.98  on 839  degrees of freedom
## Residual deviance: 721.96  on 832  degrees of freedom
## AIC: 2994
## 
## Number of Fisher Scoring iterations: 1
## 
## 
##               Theta:  0.3062 
##           Std. Err.:  0.0230 
## 
##  2 x log-likelihood:  -2976.0340
anova(m3, test="Chisq")
## Warning in anova.negbin(m3, test = "Chisq"): tests made without re-
## estimating 'theta'
## Analysis of Deviance Table
## 
## Model: Negative Binomial(0.3062), link: log
## 
## Response: Salvia
## 
## Terms added sequentially (first to last)
## 
## 
##                                  Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL                                               839     785.98         
## Microsite                         1    0.258       838     785.72  0.61139
## Nutrient                          1    3.609       837     782.11  0.05747
## arid.gradient                     1   54.596       836     727.51 1.48e-13
## Microsite:Nutrient                1    4.529       835     722.98  0.03332
## Microsite:arid.gradient           1    0.005       834     722.98  0.94599
## Nutrient:arid.gradient            1    0.870       833     722.11  0.35089
## Microsite:Nutrient:arid.gradient  1    0.145       832     721.96  0.70327
##                                     
## NULL                                
## Microsite                           
## Nutrient                         .  
## arid.gradient                    ***
## Microsite:Nutrient               *  
## Microsite:arid.gradient             
## Nutrient:arid.gradient              
## Microsite:Nutrient:arid.gradient    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
lsmeans(m3, pairwise~Microsite*Nutrient)
## NOTE: Results may be misleading due to involvement in interactions
## $lsmeans
##  Microsite Nutrient    lsmean        SE df asymp.LCL asymp.UCL
##  open      Added    0.6995864 0.1351555 NA 0.4346865 0.9644863
##  shrub     Added    0.4610742 0.1382722 NA 0.1900656 0.7320828
##  open      Ambient  0.5839160 0.1380287 NA 0.3133848 0.8544472
##  shrub     Ambient  0.9312613 0.1338667 NA 0.6688874 1.1936351
## 
## Results are given on the log (not the response) scale. 
## Confidence level used: 0.95 
## 
## $contrasts
##  contrast                       estimate        SE df z.ratio p.value
##  open,Added - shrub,Added      0.2385122 0.1933552 NA   1.234  0.6054
##  open,Added - open,Ambient     0.1156704 0.1931811 NA   0.599  0.9325
##  open,Added - shrub,Ambient   -0.2316749 0.1902296 NA  -1.218  0.6154
##  shrub,Added - open,Ambient   -0.1228418 0.1953743 NA  -0.629  0.9228
##  shrub,Added - shrub,Ambient  -0.4701871 0.1924565 NA  -2.443  0.0692
##  open,Ambient - shrub,Ambient -0.3473453 0.1922816 NA  -1.806  0.2702
## 
## Results are given on the log (not the response) scale. 
## P value adjustment: tukey method for comparing a family of 4 estimates
## treatment plots
census.long <- gather(census.arid, species, biomass, 12:14)
census.long[,"species"] <- as.factor(census.long$species)

## microsite average
census.micro <- census.long %>% filter(biomass>0) %>%  group_by(Microsite, species) %>%  summarize(Biomass=mean(biomass), error=se(biomass))

ggplot(census.micro, aes(x=species, y=Biomass, fill=Microsite)) +   geom_bar(position=position_dodge(), stat="identity", colour="black") + scale_fill_brewer()+  geom_errorbar(aes(ymin=Biomass-error, ymax=Biomass+error),
                  width=.2,                    # Width of the error bars
                  position=position_dodge(.9))+ theme_Publication()
## Warning: `legend.margin` must be specified using `margin()`. For the old
## behavior use legend.spacing
## Warning in grid.Call(L_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database

## Warning in grid.Call(L_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
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## Warning in grid.Call(L_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
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## Warning in grid.Call.graphics(L_text, as.graphicsAnnot(x$label), x$x, x
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## nutrient average
census.nut <- census.long %>% filter(biomass>0) %>%  group_by(Nutrient, species) %>%  summarize(Biomass=mean(biomass), error=se(biomass))

ggplot(census.nut, aes(x=species, y=Biomass, fill=Nutrient)) +   geom_bar(position=position_dodge(), stat="identity",colour="black") + scale_fill_brewer()+  geom_errorbar(aes(ymin=Biomass-error, ymax=Biomass+error),
                  width=.2,                    # Width of the error bars
                  position=position_dodge(.9))+ theme_Publication()
## Warning: `legend.margin` must be specified using `margin()`. For the old
## behavior use legend.spacing
## Warning in grid.Call(L_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database

## Warning in grid.Call(L_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
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## Warning in grid.Call(L_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
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## Warning in grid.Call(L_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
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## Warning in grid.Call(L_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
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## Warning in grid.Call(L_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
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## Warning in grid.Call(L_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
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## Warning in grid.Call(L_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
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## Warning in grid.Call(L_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
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## Warning in grid.Call.graphics(L_text, as.graphicsAnnot(x$label), x$x, x
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## aridity average

ggplot(census.long, aes(x=arid.gradient, y=biomass, fill=species, colour=species)) + theme_Publication()+ stat_smooth(method="lm", formula= y~exp(x),aes(x=arid.gradient, y=biomass), color="#009E73", fill="#009E7340", data=subset(census.long, species=="Phacelia.biomass" & biomass>0)) + stat_smooth(method="lm", formula= y~exp(x),aes(x=arid.gradient, y=biomass), color="#E69F00",fill="#E69F0040", data=subset(census.long, species=="Plantago.biomass" & biomass>0))+ stat_smooth(method="lm", formula= y~exp(x),aes(x=arid.gradient, y=biomass), color="#56B4E9", fill="#56B4E940", data=subset(census.long, species=="Salvia.biomass" & biomass>0))
## Warning: `legend.margin` must be specified using `margin()`. For the old
## behavior use legend.spacing
## Warning in grid.Call(L_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
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## Warning in grid.Call(L_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
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## Warning in grid.Call.graphics(L_text, as.graphicsAnnot(x$label), x$x, x
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m1 <- lm(log(biomass)~arid.gradient, data=subset(census.long, species=="Phacelia.biomass" & biomass >0))
m2 <- lm(log(biomass)~arid.gradient, data=subset(census.long, species=="Plantago.biomass" & biomass>0))
m3 <- lm(log(biomass)~arid.gradient, data=subset(census.long, species=="Salvia.biomass" & biomass>0))

Community analysis

## Regressions To Separate Phylogenetic Attraction And Repulsion

## sppregs(community[,13:53], )

### Network analysis
library(igraph)
library(statnet)


## reload native vs non-native species
status <- read.csv("Data//ERG.specieslist.csv")

shrub.comm <- subset(community, Microsite=="shrub", select=13:53)
shrub.comm <- shrub.comm[,colSums(shrub.comm)!=0]
open.comm <- subset(community, Microsite=="open", select=13:53)
open.comm <- open.comm[,colSums(open.comm)!=0]

## Shrub
## correlation species network
network <- data.frame(cor(shrub.comm))

network[,"To"] <- colnames(network)

test <- gather(network, From, correlation, 1:(ncol(network)-1))
test <- subset(test, correlation>0.10 & correlation != 1 | correlation < -0.10 )

n.shrub <- graph_from_data_frame(test[,1:2], directed=TRUE)

## plot network circle
lo <- layout_in_circle(n.shrub)
plot(n.shrub, layout=lo)

transitivity(n.shrub, type = "local") 
##  [1] 0.16666667 0.06060606 0.13333333 0.08888889 0.00000000 0.03296703
##  [7] 0.07575758 0.10714286 0.03684211 0.06535948 0.00000000 0.07142857
## [13] 0.07142857 0.07142857 0.02222222 0.00000000 0.00000000 0.16666667
## [19] 0.00000000 0.00000000 0.10714286 0.16666667 0.00000000 0.13333333
## [25] 0.06666667 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
## [31] 0.00000000
transitivity(n.shrub, type = "global") 
## [1] 0.2755102
## colour graphs
shapes <- V(n.shrub)$name %in% (subset(status, status=="non.native", select=1))[,1] +1
plot(n.shrub, layout=layout_on_grid(n.shrub),vertex.shape=c("circle", "square")[shapes],  vertex.color=c("tomato2", "royalblue")[shapes])

## open
## correlation species network
network <- data.frame(cor(open.comm))

network[,"To"] <- colnames(network)

test <- gather(network, From, correlation, 1:(ncol(network)-1))
test <- subset(test, correlation>0.10 & correlation != 1 | correlation < -0.10 )

n.open <- graph_from_data_frame(test[,1:2], directed=TRUE)

## plot network circle
lo <- layout_in_circle(n.open)
plot(n.open, layout=lo)

## colour graphs
shapes <- V(n.open)$name %in% (subset(status, status=="non.native", select=1))[,1] +1
plot(n.open, layout=layout_on_grid(n.open),vertex.shape=c("circle", "square")[shapes],  vertex.color=c("tomato2", "royalblue")[shapes])

## statistics
transitivity(n.open, type = "local") 
##  [1] 0.09890110 0.17857143 0.03333333 0.06666667 0.16666667 0.03921569
##  [7] 0.03030303 0.20000000 0.20000000 0.07189542 0.00000000 0.00000000
## [13] 0.00000000 0.00000000 0.06666667 0.03571429 0.06666667 0.06666667
## [19] 0.06666667 0.00000000 0.16666667 0.00000000 0.16666667 0.16666667
## [25] 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.16666667
## [31] 0.00000000 0.00000000 0.00000000
transitivity(n.open, type = "global") 
## [1] 0.2934783
### indicator species analysis
library(indicspecies)

## indicator species analysis
indval <- multipatt(community[,13:53], community$Microsite, control=how(nperm=999))
summary(indval)
## 
##  Multilevel pattern analysis
##  ---------------------------
## 
##  Association function: IndVal.g
##  Significance level (alpha): 0.05
## 
##  Total number of species: 41
##  Selected number of species: 15 
##  Number of species associated to 1 group: 15 
## 
##  List of species associated to each combination: 
## 
##  Group open  #sps.  8 
##                   stat p.value    
## E.cicutarium     0.558   0.001 ***
## L.gracilis       0.427   0.001 ***
## A.wrangelianus   0.201   0.001 ***
## C.brevipes       0.171   0.033 *  
## A.lentiginosus   0.149   0.024 *  
## C.rigida         0.149   0.018 *  
## A.grandiflora    0.129   0.016 *  
## Boraginaceae.sp. 0.120   0.032 *  
## 
##  Group shrub  #sps.  7 
##                  stat p.value    
## C.fremontii     0.379   0.001 ***
## M.glabrata      0.277   0.001 ***
## P.tanacetifolia 0.267   0.001 ***
## A.tessellata    0.199   0.004 ** 
## B.nigra         0.179   0.021 *  
## E.nitidum       0.172   0.007 ** 
## E.glyptosperma  0.128   0.043 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## species accumilation curves
accum1 <- specaccum(subset(community, Microsite=="shrub", select=13:53), method="random",  permutations=1000)
accum2 <- specaccum(subset(community, Microsite=="open", select=13:53), method="random",  permutations=1000)


plot(accum2,  ci.col="Grey20", xlab="Sampling effort", cex.axis=1.5, cex.lab=1.8, ylab="Species richness")
plot(accum1,add=T , col="Grey50",   ci.col="Grey60")

cluster analysis

library("cluster")
library("factoextra")
library("magrittr")

comm.sum <- community %>%  group_by(Microsite, Site) %>% summarize_if(is.numeric, funs(sum))
comm.sum <- data.frame(comm.sum)
rownames(comm.sum) <- paste(comm.sum$Microsite,comm.sum$Site)

## coreelation matrix

### Distance measures
res.dist <- get_dist(comm.sum[,13:53], stand = TRUE, method = "euclidean")
fviz_dist(res.dist,    gradient = list(low = "#00AFBB", mid = "white", high = "#FC4E07"))

## dendrogram

## transform data
comm.trans <- decostand(comm.sum[,13:53], "hell")

## clean data for ordination
## see distribution of spp
boxplot(comm.trans, xaxt="n")
labs <- colnames(comm.trans)
text(cex=0.8, x=1:41-1, y=-0.12, labs, xpd=TRUE, srt=45)

## remove spp with only one instancve
comm.trans <- comm.trans[,!colSums(comm.trans)==apply(comm.trans, 2, max)]


## replace outliers with mean
avg.max <- function(x) {
  y =  max(x)
  avg = mean(x)
  x[x==y] <- avg
  return(x)
}
  
## dot chart to find outliers (remove or Avg)
#dotchart(comm.trans$E.cicutarium) ## Erodium
comm.trans[,"E.cicutarium"] <- avg.max(comm.trans$E.cicutarium) ## removed outlier
#dotchart(comm.trans$A.wrangelianus) ## Chilean trefoil
comm.trans[,"A.wrangelianus"] <- NULL ## removed entire species
#dotchart(comm.trans$A.lentiginosus) ##
comm.trans[,"A.lentiginosus"] <- avg.max(comm.trans$A.lentiginosus) ## removed outlier
#dotchart(comm.trans$H.vulgare) ## Hordeum
comm.trans[,"H.vulgare"] <- NULL ## removed entire species
#dotchart(comm.trans$Pectocarya.spp) ## 
comm.trans[,"Pectocarya.spp"] <- avg.max(comm.trans$Pectocarya.spp) ## removed outlier
#dotchart(comm.trans$L.arizonicus) ## Lupin 
comm.trans[,"L.arizonicus"] <- NULL ## removed entire species
#dotchart(comm.trans$M.bellioides) ##
comm.trans[,"M.bellioides"] <- NULL ## removed entire species
#dotchart(comm.trans$B.nigra) ## Mustard
comm.trans[,"B.nigra"] <- NULL ## removed entire species
#dotchart(comm.trans$N.demissum) ## Sand Matt
comm.trans[,"N.demissum"] <- avg.max(comm.trans$N.demissum) ## removed outlier
#dotchart(comm.trans$L.gracilis) ## 
comm.trans[,"L.gracilis"] <- avg.max(comm.trans$L.gracilis) ## removed outlier
#dotchart(comm.trans$A.grandiflora) ##  Amsinckia
comm.trans[,"A.grandiflora"] <- NULL ## removed entire species
#dotchart(comm.trans$B.diandrus) ##  Bromus diandrus
comm.trans[,"B.diandrus"] <- NULL ## removed entire species
#dotchart(comm.trans$M.affinis) ##  
comm.trans[,"M.affinis"] <- NULL ## removed entire species
#dotchart(comm.trans$P.crenulata) ##  
comm.trans[,"P.crenulata"] <- NULL ## removed entire species
#dotchart(comm.trans$Erinoginum.spp) # buckwheat
comm.trans[,"Erinoginum.spp"] <- avg.max(comm.trans$Erinoginum.spp) ## removed outlier
#dotchart(comm.trans$C.lasiophyllus) # 
comm.trans[,"C.lasiophyllus"] <- NULL ## removed entire species



library(usdm)
library(corrplot)

## Check for collinearity
vifcor(comm.trans)
## 4 variables from the 25 input variables have collinearity problem: 
##  
## E.wallacei P.insularis E.glyptosperma L.decorus 
## 
## After excluding the collinear variables, the linear correlation coefficients ranges between: 
## min correlation ( L.gracilis ~ S.barbatus ):  -0.003266766 
## max correlation ( C.fremontii ~ M.glabrata ):  0.8450794 
## 
## ---------- VIFs of the remained variables -------- 
##          Variables VIF
## 1         B.rubens Inf
## 2     E.cicutarium Inf
## 3       S.barbatus Inf
## 4      C.barbigera Inf
## 5    C.claviformis Inf
## 6   A.lentiginosus Inf
## 7         C.rigida Inf
## 8  P.tanacetifolia Inf
## 9   Pectocarya.spp Inf
## 10     D.capitatum Inf
## 11      C.brevipes Inf
## 12      M.glabrata Inf
## 13     C.fremontii Inf
## 14    C.intermedia Inf
## 15      N.demissum Inf
## 16      L.gracilis Inf
## 17    A.tessellata Inf
## 18       P.secunda Inf
## 19  L.sparsiflorus Inf
## 20  Erinoginum.spp Inf
## 21       E.nitidum Inf
## drop Claviformis because of collinear problems in P.insularis
comm.trans[,"C.claviformis"] <- NULL ## removed entire species
comm.trans[,"L.decorus"] <- NULL ## removed entire species
comm.trans[,"E.glyptosperma"] <- NULL ## removed entire species
comm.trans[,"E.wallacei"] <- NULL ## removed entire species

cor.dat <- cor(comm.trans[,1:20])
corrplot(cor.dat, method="number")

## calculate distances
dis <- vegdist(comm.trans, method="bray")


## cluster analysis
clus <- hclust(dis, "ward.D2")

## cluster colours
colz <- c("#3e4444", "#86af49", "#405d27","#c1946a")

fviz_dend(clus, k = 4, # Cut in four groups
          cex = 0.5, # label size
          k_colors = colz,
          color_labels_by_k = TRUE, # color labels by groups
          rect = TRUE # Add rectangle around groups
          )

grp <- cutree(clus, 4)

## Interpretation of classes

## get soil moisture from site/microsite
smc.avg <- smc.end %>% group_by(Site, Microsite) %>% summarize(mean.smc=mean(swc.avg, na.rm=T))
smc.avg <- data.frame(smc.avg)
smc.avg[,"grp"] <- as.factor(c(3,3,2,1,4,4,1,1,4,4,3,3,2,2))

boxplot(mean.smc ~ grp, data=smc.avg)

## conduct ordination
ord <- rda(comm.trans)
(R2adj <- RsquareAdj(rda1)$adj.r.squared) ## 50.4% of variation explained
## [1] 0.3871872
## calculate priority
spp.priority <- colSums(comm.trans)

## plot ordination
par(mar=c(4.5,4.5,0.5,0.5))
plot(ord, type="n")
ordiellipse(ord, grp, lty = 2, col = colz, draw="polygon", alpha=150)
orditorp(ord, display = "species", cex = 0.7, col = "darkred", priority=spp.priority, air=0.5)
orditorp(ord, display = "sites", cex = 0.7, col = "black")

##collect environmental variables for site
nutrients <- read.csv("Data/ERG.soilnutrients.csv")
nutrients.mean <- nutrients %>% group_by(microsite, Site) %>% summarise_all(funs(mean))
nutrients.vars <- data.frame(nutrients.mean)


## summarize daily means across sites
means <- HOBO.data %>% group_by(Microsite, Site) %>% summarize(Temp.=mean(Temp, na.rm=T),rh=mean(RH, na.rm=T),temp.se=se(Temp),rh.se=se(RH))
means <- data.frame(means)

## soil moisture
smc.avg <- smc.early %>% group_by(Microsite, Site) %>% summarize(SMC=mean(swc.avg, na.rm=T))

envs <- data.frame(swc=smc.avg[,"SMC"],nutrients.vars[,c("N","P","K")], means[,c("Temp.","rh")])

## Check for collinearity
cor(envs)
##              SMC           N           P           K      Temp.         rh
## SMC    1.0000000 -0.31692385  0.44854712  0.81694831 -0.6306962  0.9176776
## N     -0.3169238  1.00000000  0.05879919 -0.05275422  0.4188434 -0.4374109
## P      0.4485471  0.05879919  1.00000000  0.64885814 -0.5264257  0.5353719
## K      0.8169483 -0.05275422  0.64885814  1.00000000 -0.5705341  0.7740852
## Temp. -0.6306962  0.41884336 -0.52642569 -0.57053407  1.0000000 -0.8625519
## rh     0.9176776 -0.43741089  0.53537193  0.77408521 -0.8625519  1.0000000
envs[,"K"] <- NULL ## drop potassium for being correlated
envs[,"rh"] <- NULL ## drop humidity for being correlated

ord.env <- envfit(ord, envs)
plot(ord.env)

# ordicluster(ord, clus, col="blue")

Basic community data

community
##     Year  ID            Site Rep      Lat      Long Elevation Gradient
## 1   2017   1    PanocheHills   1 36.70001 -120.8011  656.3209        1
## 2   2017   1    PanocheHills   1 36.70001 -120.8011  656.3209        1
## 3   2017   2    PanocheHills   2 36.70005 -120.8012  656.1902        1
## 4   2017   2    PanocheHills   2 36.70005 -120.8012  656.1902        1
## 5   2017   3    PanocheHills   3 36.70010 -120.8015  656.6475        1
## 6   2017   3    PanocheHills   3 36.70010 -120.8015  656.6475        1
## 7   2017   4    PanocheHills   4 36.70012 -120.8014  657.3752        1
## 8   2017   4    PanocheHills   4 36.70012 -120.8014  657.3752        1
## 9   2017   5    PanocheHills   5 36.70019 -120.8016  656.5075        1
## 10  2017   5    PanocheHills   5 36.70019 -120.8016  656.5075        1
## 11  2017   6    PanocheHills   6 36.70026 -120.8014  655.5837        1
## 12  2017   6    PanocheHills   6 36.70026 -120.8014  655.5837        1
## 13  2017   7    PanocheHills   7 36.70037 -120.8014  655.4344        1
## 14  2017   7    PanocheHills   7 36.70037 -120.8014  655.4344        1
## 15  2017   8    PanocheHills   8 36.70045 -120.8015  655.3132        1
## 16  2017   8    PanocheHills   8 36.70045 -120.8015  655.3132        1
## 17  2017   9    PanocheHills   9 36.70052 -120.8015  655.0519        1
## 18  2017   9    PanocheHills   9 36.70052 -120.8015  655.0519        1
## 19  2017  10    PanocheHills  10 36.70044 -120.8016  655.5371        1
## 20  2017  10    PanocheHills  10 36.70044 -120.8016  655.5371        1
## 21  2017  11    PanocheHills  11 36.70057 -120.8017  656.4329        1
## 22  2017  11    PanocheHills  11 36.70057 -120.8017  656.4329        1
## 23  2017  12    PanocheHills  12 36.70063 -120.8019  657.0300        1
## 24  2017  12    PanocheHills  12 36.70063 -120.8019  657.0300        1
## 25  2017  13    PanocheHills  13 36.70060 -120.8020  657.1606        1
## 26  2017  13    PanocheHills  13 36.70060 -120.8020  657.1606        1
## 27  2017  14    PanocheHills  14 36.70064 -120.8017  656.7687        1
## 28  2017  14    PanocheHills  14 36.70064 -120.8017  656.7687        1
## 29  2017  15    PanocheHills  15 36.70064 -120.8022  657.2166        1
## 30  2017  15    PanocheHills  15 36.70064 -120.8022  657.2166        1
## 31  2017  16    PanocheHills  16 36.70074 -120.8022  656.9554        1
## 32  2017  16    PanocheHills  16 36.70074 -120.8022  656.9554        1
## 33  2017  17    PanocheHills  17 36.70086 -120.8022  656.0223        1
## 34  2017  17    PanocheHills  17 36.70086 -120.8022  656.0223        1
## 35  2017  18    PanocheHills  18 36.70080 -120.8023  656.5541        1
## 36  2017  18    PanocheHills  18 36.70080 -120.8023  656.5541        1
## 37  2017  19    PanocheHills  19 36.70073 -120.8024  656.1996        1
## 38  2017  19    PanocheHills  19 36.70073 -120.8024  656.1996        1
## 39  2017  20    PanocheHills  20 36.70069 -120.8023  655.4438        1
## 40  2017  20    PanocheHills  20 36.70069 -120.8023  655.4438        1
## 41  2017  21    PanocheHills  21 36.70053 -120.8023  655.8543        1
## 42  2017  21    PanocheHills  21 36.70053 -120.8023  655.8543        1
## 43  2017  22    PanocheHills  22 36.70048 -120.8021  657.1420        1
## 44  2017  22    PanocheHills  22 36.70048 -120.8021  657.1420        1
## 45  2017  23    PanocheHills  23 36.70042 -120.8021  657.7485        1
## 46  2017  23    PanocheHills  23 36.70042 -120.8021  657.7485        1
## 47  2017  24    PanocheHills  24 36.70036 -120.8020  658.2523        1
## 48  2017  24    PanocheHills  24 36.70036 -120.8020  658.2523        1
## 49  2017  25    PanocheHills  25 36.70035 -120.8019  658.7189        1
## 50  2017  25    PanocheHills  25 36.70035 -120.8019  658.7189        1
## 51  2017  26    PanocheHills  26 36.70026 -120.8020  658.9055        1
## 52  2017  26    PanocheHills  26 36.70026 -120.8020  658.9055        1
## 53  2017  27    PanocheHills  27 36.70013 -120.8020  658.9988        1
## 54  2017  27    PanocheHills  27 36.70013 -120.8020  658.9988        1
## 55  2017  28    PanocheHills  28 36.70015 -120.8020  659.0361        1
## 56  2017  28    PanocheHills  28 36.70015 -120.8020  659.0361        1
## 57  2017  29    PanocheHills  29 36.70009 -120.8019  658.9614        1
## 58  2017  29    PanocheHills  29 36.70009 -120.8019  658.9614        1
## 59  2017  30    PanocheHills  30 36.70005 -120.8019  659.1201        1
## 60  2017  30    PanocheHills  30 36.70005 -120.8019  659.1201        1
## 61  2017  31          Cuyama   1 34.85522 -119.4885  806.8343        2
## 62  2017  31          Cuyama   1 34.85522 -119.4885  806.8343        2
## 63  2017  32          Cuyama   2 34.85518 -119.4886  806.5264        2
## 64  2017  32          Cuyama   2 34.85518 -119.4886  806.5264        2
## 65  2017  33          Cuyama   3 34.85512 -119.4886  806.4984        2
## 66  2017  33          Cuyama   3 34.85512 -119.4886  806.4984        2
## 67  2017  34          Cuyama   4 34.85506 -119.4885  806.6196        2
## 68  2017  34          Cuyama   4 34.85506 -119.4885  806.6196        2
## 69  2017  35          Cuyama   5 34.85505 -119.4884  807.0115        2
## 70  2017  35          Cuyama   5 34.85505 -119.4884  807.0115        2
## 71  2017  36          Cuyama   6 34.85500 -119.4884  806.0505        2
## 72  2017  36          Cuyama   6 34.85500 -119.4884  806.0505        2
## 73  2017  37          Cuyama   7 34.85489 -119.4885  808.0753        2
## 74  2017  37          Cuyama   7 34.85489 -119.4885  808.0753        2
## 75  2017  38          Cuyama   8 34.85488 -119.4884  808.7097        2
## 76  2017  38          Cuyama   8 34.85488 -119.4884  808.7097        2
## 77  2017  39          Cuyama   9 34.85489 -119.4883  808.5231        2
## 78  2017  39          Cuyama   9 34.85489 -119.4883  808.5231        2
## 79  2017  40          Cuyama  10 34.85476 -119.4884  809.9227        2
## 80  2017  40          Cuyama  10 34.85476 -119.4884  809.9227        2
## 81  2017  41          Cuyama  11 34.85462 -119.4885  809.3256        2
## 82  2017  41          Cuyama  11 34.85462 -119.4885  809.3256        2
## 83  2017  42          Cuyama  12 34.85465 -119.4884  809.5961        2
## 84  2017  42          Cuyama  12 34.85465 -119.4884  809.5961        2
## 85  2017  43          Cuyama  13 34.85454 -119.4884  810.1840        2
## 86  2017  43          Cuyama  13 34.85454 -119.4884  810.1840        2
## 87  2017  44          Cuyama  14 34.85442 -119.4884  809.8107        2
## 88  2017  44          Cuyama  14 34.85442 -119.4884  809.8107        2
## 89  2017  45          Cuyama  15 34.85441 -119.4882  810.0720        2
## 90  2017  45          Cuyama  15 34.85441 -119.4882  810.0720        2
## 91  2017  46          Cuyama  16 34.85436 -119.4881  810.3986        2
## 92  2017  46          Cuyama  16 34.85436 -119.4881  810.3986        2
## 93  2017  47          Cuyama  17 34.85443 -119.4880  810.0161        2
## 94  2017  47          Cuyama  17 34.85443 -119.4880  810.0161        2
## 95  2017  48          Cuyama  18 34.85449 -119.4880  811.1917        2
## 96  2017  48          Cuyama  18 34.85449 -119.4880  811.1917        2
## 97  2017  49          Cuyama  19 34.85461 -119.4879  811.6675        2
## 98  2017  49          Cuyama  19 34.85461 -119.4879  811.6675        2
## 99  2017  50          Cuyama  20 34.85472 -119.4878  809.8854        2
## 100 2017  50          Cuyama  20 34.85472 -119.4878  809.8854        2
## 101 2017  51          Cuyama  21 34.85472 -119.4877  810.4172        2
## 102 2017  51          Cuyama  21 34.85472 -119.4877  810.4172        2
## 103 2017  52          Cuyama  22 34.85484 -119.4876  811.3410        2
## 104 2017  52          Cuyama  22 34.85484 -119.4876  811.3410        2
## 105 2017  53          Cuyama  23 34.85492 -119.4874  810.9584        2
## 106 2017  53          Cuyama  23 34.85492 -119.4874  810.9584        2
## 107 2017  54          Cuyama  24 34.85496 -119.4873  811.6302        2
## 108 2017  54          Cuyama  24 34.85496 -119.4873  811.6302        2
## 109 2017  55          Cuyama  25 34.85500 -119.4874  811.9008        2
## 110 2017  55          Cuyama  25 34.85500 -119.4874  811.9008        2
## 111 2017  56          Cuyama  26 34.85510 -119.4875  812.1807        2
## 112 2017  56          Cuyama  26 34.85510 -119.4875  812.1807        2
## 113 2017  57          Cuyama  27 34.85511 -119.4876  812.1248        2
## 114 2017  57          Cuyama  27 34.85511 -119.4876  812.1248        2
## 115 2017  58          Cuyama  28 34.85508 -119.4876  812.6193        2
## 116 2017  58          Cuyama  28 34.85508 -119.4876  812.6193        2
## 117 2017  59          Cuyama  29 34.85499 -119.4879  811.4623        2
## 118 2017  59          Cuyama  29 34.85499 -119.4879  811.4623        2
## 119 2017  60          Cuyama  30 34.85502 -119.4881  811.7795        2
## 120 2017  60          Cuyama  30 34.85502 -119.4881  811.7795        2
## 121 2017  61         Barstow   1 35.09405 -116.8349  496.0200        4
## 122 2017  61         Barstow   1 35.09405 -116.8349  496.0200        4
## 123 2017  62         Barstow   2 35.09391 -116.8348  496.1690        4
## 124 2017  62         Barstow   2 35.09391 -116.8348  496.1690        4
## 125 2017  63         Barstow   3 35.09381 -116.8349  495.4130        4
## 126 2017  63         Barstow   3 35.09381 -116.8349  495.4130        4
## 127 2017  64         Barstow   4 35.09373 -116.8350  495.4600        4
## 128 2017  64         Barstow   4 35.09373 -116.8350  495.4600        4
## 129 2017  65         Barstow   5 35.09372 -116.8350  494.9840        4
## 130 2017  65         Barstow   5 35.09372 -116.8350  494.9840        4
## 131 2017  66         Barstow   6 35.09361 -116.8350  494.7600        4
## 132 2017  66         Barstow   6 35.09361 -116.8350  494.7600        4
## 133 2017  67         Barstow   7 35.09359 -116.8351  495.0870        4
## 134 2017  67         Barstow   7 35.09359 -116.8351  495.0870        4
## 135 2017  68         Barstow   8 35.09358 -116.8351  495.1050        4
## 136 2017  68         Barstow   8 35.09358 -116.8351  495.1050        4
## 137 2017  69         Barstow   9 35.09353 -116.8351  495.1520        4
## 138 2017  69         Barstow   9 35.09353 -116.8351  495.1520        4
## 139 2017  70         Barstow  10 35.09342 -116.8350  494.9280        4
## 140 2017  70         Barstow  10 35.09342 -116.8350  494.9280        4
## 141 2017  71         Barstow  11 35.09338 -116.8352  494.8630        4
## 142 2017  71         Barstow  11 35.09338 -116.8352  494.8630        4
## 143 2017  72         Barstow  12 35.09336 -116.8351  494.9000        4
## 144 2017  72         Barstow  12 35.09336 -116.8351  494.9000        4
## 145 2017  73         Barstow  13 35.09332 -116.8351  494.8910        4
## 146 2017  73         Barstow  13 35.09332 -116.8351  494.8910        4
## 147 2017  74         Barstow  14 35.09325 -116.8352  494.5830        4
## 148 2017  74         Barstow  14 35.09325 -116.8352  494.5830        4
## 149 2017  75         Barstow  15 35.09318 -116.8352  495.0680        4
## 150 2017  75         Barstow  15 35.09318 -116.8352  495.0680        4
## 151 2017  76         Barstow  16 35.09313 -116.8352  494.8810        4
## 152 2017  76         Barstow  16 35.09313 -116.8352  494.8810        4
## 153 2017  77         Barstow  17 35.09310 -116.8353  494.6010        4
## 154 2017  77         Barstow  17 35.09310 -116.8353  494.6010        4
## 155 2017  78         Barstow  18 35.09299 -116.8352  494.1160        4
## 156 2017  78         Barstow  18 35.09299 -116.8352  494.1160        4
## 157 2017  79         Barstow  19 35.09289 -116.8352  493.9670        4
## 158 2017  79         Barstow  19 35.09289 -116.8352  493.9670        4
## 159 2017  80         Barstow  20 35.09276 -116.8352  493.7340        4
## 160 2017  80         Barstow  20 35.09276 -116.8352  493.7340        4
## 161 2017  81         Barstow  21 35.09275 -116.8351  573.0444        4
## 162 2017  81         Barstow  21 35.09275 -116.8351  573.0444        4
## 163 2017  82         Barstow  22 35.09271 -116.8350  572.1393        4
## 164 2017  82         Barstow  22 35.09271 -116.8350  572.1393        4
## 165 2017  83         Barstow  23 35.09280 -116.8349  572.9885        4
## 166 2017  83         Barstow  23 35.09280 -116.8349  572.9885        4
## 167 2017  84         Barstow  24 35.09275 -116.8348  572.3166        4
## 168 2017  84         Barstow  24 35.09275 -116.8348  572.3166        4
## 169 2017  85         Barstow  25 35.09280 -116.8347  571.7568        4
## 170 2017  85         Barstow  25 35.09280 -116.8347  571.7568        4
## 171 2017  86         Barstow  26 35.09287 -116.8348  571.9061        4
## 172 2017  86         Barstow  26 35.09287 -116.8348  571.9061        4
## 173 2017  87         Barstow  27 35.09290 -116.8346  571.6915        4
## 174 2017  87         Barstow  27 35.09290 -116.8346  571.6915        4
## 175 2017  88         Barstow  28 35.09295 -116.8345  571.9341        4
## 176 2017  88         Barstow  28 35.09295 -116.8345  571.9341        4
## 177 2017  89         Barstow  29 35.09302 -116.8347  571.4862        4
## 178 2017  89         Barstow  29 35.09302 -116.8347  571.4862        4
## 179 2017  90         Barstow  30 35.09311 -116.8347  571.7195        4
## 180 2017  90         Barstow  30 35.09311 -116.8347  571.7195        4
## 181 2017  91   HeartofMojave   1 34.69820 -115.6842  784.7300        5
## 182 2017  91   HeartofMojave   1 34.69820 -115.6842  784.7300        5
## 183 2017  92   HeartofMojave   2 34.69811 -115.6841  784.6460        5
## 184 2017  92   HeartofMojave   2 34.69811 -115.6841  784.6460        5
## 185 2017  93   HeartofMojave   3 34.69805 -115.6841  784.8510        5
## 186 2017  93   HeartofMojave   3 34.69805 -115.6841  784.8510        5
## 187 2017  94   HeartofMojave   4 34.69798 -115.6841  784.8330        5
## 188 2017  94   HeartofMojave   4 34.69798 -115.6841  784.8330        5
## 189 2017  95   HeartofMojave   5 34.69792 -115.6841  784.5530        5
## 190 2017  95   HeartofMojave   5 34.69792 -115.6841  784.5530        5
## 191 2017  96   HeartofMojave   6 34.69794 -115.6843  784.2630        5
## 192 2017  96   HeartofMojave   6 34.69794 -115.6843  784.2630        5
## 193 2017  97   HeartofMojave   7 34.69789 -115.6843  784.3660        5
## 194 2017  97   HeartofMojave   7 34.69789 -115.6843  784.3660        5
## 195 2017  98   HeartofMojave   8 34.69784 -115.6844  784.2350        5
## 196 2017  98   HeartofMojave   8 34.69784 -115.6844  784.2350        5
## 197 2017  99   HeartofMojave   9 34.69782 -115.6843  783.9930        5
## 198 2017  99   HeartofMojave   9 34.69782 -115.6843  783.9930        5
## 199 2017 100   HeartofMojave  10 34.69779 -115.6844  783.2460        5
## 200 2017 100   HeartofMojave  10 34.69779 -115.6844  783.2460        5
## 201 2017 101   HeartofMojave  11 34.69776 -115.6843  783.0600        5
## 202 2017 101   HeartofMojave  11 34.69776 -115.6843  783.0600        5
## 203 2017 102   HeartofMojave  12 34.69772 -115.6844  783.0410        5
## 204 2017 102   HeartofMojave  12 34.69772 -115.6844  783.0410        5
## 205 2017 103   HeartofMojave  13 34.69762 -115.6844  783.1720        5
## 206 2017 103   HeartofMojave  13 34.69762 -115.6844  783.1720        5
## 207 2017 104   HeartofMojave  14 34.69764 -115.6845  782.6680        5
## 208 2017 104   HeartofMojave  14 34.69764 -115.6845  782.6680        5
## 209 2017 105   HeartofMojave  15 34.69756 -115.6844  782.5840        5
## 210 2017 105   HeartofMojave  15 34.69756 -115.6844  782.5840        5
## 211 2017 106   HeartofMojave  16 34.69747 -115.6843  781.8750        5
## 212 2017 106   HeartofMojave  16 34.69747 -115.6843  781.8750        5
## 213 2017 107   HeartofMojave  17 34.69735 -115.6843  783.0130        5
## 214 2017 107   HeartofMojave  17 34.69735 -115.6843  783.0130        5
## 215 2017 108   HeartofMojave  18 34.69730 -115.6843  782.3690        5
## 216 2017 108   HeartofMojave  18 34.69730 -115.6843  782.3690        5
## 217 2017 109   HeartofMojave  19 34.69718 -115.6843  781.3430        5
## 218 2017 109   HeartofMojave  19 34.69718 -115.6843  781.3430        5
## 219 2017 110   HeartofMojave  20 34.69714 -115.6843  781.0820        5
## 220 2017 110   HeartofMojave  20 34.69714 -115.6843  781.0820        5
## 221 2017 111   HeartofMojave  21 34.69721 -115.6842  770.7805        5
## 222 2017 111   HeartofMojave  21 34.69721 -115.6842  770.7805        5
## 223 2017 112   HeartofMojave  22 34.69729 -115.6841  770.9671        5
## 224 2017 112   HeartofMojave  22 34.69729 -115.6841  770.9671        5
## 225 2017 113   HeartofMojave  23 34.69745 -115.6838  773.9343        5
## 226 2017 113   HeartofMojave  23 34.69745 -115.6838  773.9343        5
## 227 2017 114   HeartofMojave  24 34.69748 -115.6838  773.0945        5
## 228 2017 114   HeartofMojave  24 34.69748 -115.6838  773.0945        5
## 229 2017 115   HeartofMojave  25 34.69748 -115.6837  773.9996        5
## 230 2017 115   HeartofMojave  25 34.69748 -115.6837  773.9996        5
## 231 2017 116   HeartofMojave  26 34.69755 -115.6837  774.5034        5
## 232 2017 116   HeartofMojave  26 34.69755 -115.6837  774.5034        5
## 233 2017 117   HeartofMojave  27 34.69761 -115.6836  774.8766        5
## 234 2017 117   HeartofMojave  27 34.69761 -115.6836  774.8766        5
## 235 2017 118   HeartofMojave  28 34.69764 -115.6836  775.0259        5
## 236 2017 118   HeartofMojave  28 34.69764 -115.6836  775.0259        5
## 237 2017 119   HeartofMojave  29 34.69769 -115.6837  774.9606        5
## 238 2017 119   HeartofMojave  29 34.69769 -115.6837  774.9606        5
## 239 2017 120   HeartofMojave  30 34.69772 -115.6837  775.0726        5
## 240 2017 120   HeartofMojave  30 34.69772 -115.6837  775.0726        5
## 241 2017 121 SheepholeValley   1 34.20568 -115.7197  545.9200        6
## 242 2017 121 SheepholeValley   1 34.20568 -115.7197  545.9200        6
## 243 2017 122 SheepholeValley   2 34.20574 -115.7195  546.5730        6
## 244 2017 122 SheepholeValley   2 34.20574 -115.7195  546.5730        6
## 245 2017 123 SheepholeValley   3 34.20565 -115.7194  546.6290        6
## 246 2017 123 SheepholeValley   3 34.20565 -115.7194  546.6290        6
## 247 2017 124 SheepholeValley   4 34.20559 -115.7192  546.2470        6
## 248 2017 124 SheepholeValley   4 34.20559 -115.7192  546.2470        6
## 249 2017 125 SheepholeValley   5 34.20563 -115.7192  546.5170        6
## 250 2017 125 SheepholeValley   5 34.20563 -115.7192  546.5170        6
## 251 2017 126 SheepholeValley   6 34.20558 -115.7190  546.2930        6
## 252 2017 126 SheepholeValley   6 34.20558 -115.7190  546.2930        6
## 253 2017 127 SheepholeValley   7 34.20561 -115.7190  545.8920        6
## 254 2017 127 SheepholeValley   7 34.20561 -115.7190  545.8920        6
## 255 2017 128 SheepholeValley   8 34.20555 -115.7190  545.7430        6
## 256 2017 128 SheepholeValley   8 34.20555 -115.7190  545.7430        6
## 257 2017 129 SheepholeValley   9 34.20572 -115.7188  546.5080        6
## 258 2017 129 SheepholeValley   9 34.20572 -115.7188  546.5080        6
## 259 2017 130 SheepholeValley  10 34.20577 -115.7187  546.9090        6
## 260 2017 130 SheepholeValley  10 34.20577 -115.7187  546.9090        6
## 261 2017 131 SheepholeValley  11 34.20580 -115.7186  547.1330        6
## 262 2017 131 SheepholeValley  11 34.20580 -115.7186  547.1330        6
## 263 2017 132 SheepholeValley  12 34.20594 -115.7186  547.8890        6
## 264 2017 132 SheepholeValley  12 34.20594 -115.7186  547.8890        6
## 265 2017 133 SheepholeValley  13 34.20611 -115.7187  548.4580        6
## 266 2017 133 SheepholeValley  13 34.20611 -115.7187  548.4580        6
## 267 2017 134 SheepholeValley  14 34.20626 -115.7190  549.5310        6
## 268 2017 134 SheepholeValley  14 34.20626 -115.7190  549.5310        6
## 269 2017 135 SheepholeValley  15 34.20632 -115.7192  550.0070        6
## 270 2017 135 SheepholeValley  15 34.20632 -115.7192  550.0070        6
## 271 2017 136 SheepholeValley  16 34.20631 -115.7194  550.3800        6
## 272 2017 136 SheepholeValley  16 34.20631 -115.7194  550.3800        6
## 273 2017 137 SheepholeValley  17 34.20628 -115.7196  549.9420        6
## 274 2017 137 SheepholeValley  17 34.20628 -115.7196  549.9420        6
## 275 2017 138 SheepholeValley  18 34.20625 -115.7196  550.1560        6
## 276 2017 138 SheepholeValley  18 34.20625 -115.7196  550.1560        6
## 277 2017 139 SheepholeValley  19 34.20627 -115.7197  549.4560        6
## 278 2017 139 SheepholeValley  19 34.20627 -115.7197  549.4560        6
## 279 2017 140 SheepholeValley  20 34.20618 -115.7198  549.1950        6
## 280 2017 140 SheepholeValley  20 34.20618 -115.7198  549.1950        6
## 281 2017 141 SheepholeValley  21 34.20650 -115.7194  598.3586        6
## 282 2017 141 SheepholeValley  21 34.20650 -115.7194  598.3586        6
## 283 2017 142 SheepholeValley  22 34.20652 -115.7192  597.8641        6
## 284 2017 142 SheepholeValley  22 34.20652 -115.7192  597.8641        6
## 285 2017 143 SheepholeValley  23 34.20664 -115.7191  599.4130        6
## 286 2017 143 SheepholeValley  23 34.20664 -115.7191  599.4130        6
## 287 2017 144 SheepholeValley  24 34.20678 -115.7192  600.1594        6
## 288 2017 144 SheepholeValley  24 34.20678 -115.7192  600.1594        6
## 289 2017 145 SheepholeValley  25 34.20664 -115.7192  597.9014        6
## 290 2017 145 SheepholeValley  25 34.20664 -115.7192  597.9014        6
## 291 2017 146 SheepholeValley  26 34.20659 -115.7193  597.4069        6
## 292 2017 146 SheepholeValley  26 34.20659 -115.7193  597.4069        6
## 293 2017 147 SheepholeValley  27 34.20659 -115.7195  595.8207        6
## 294 2017 147 SheepholeValley  27 34.20659 -115.7195  595.8207        6
## 295 2017 148 SheepholeValley  28 34.20658 -115.7198  595.4101        6
## 296 2017 148 SheepholeValley  28 34.20658 -115.7198  595.4101        6
## 297 2017 149 SheepholeValley  29 34.20655 -115.7199  595.5687        6
## 298 2017 149 SheepholeValley  29 34.20655 -115.7199  595.5687        6
## 299 2017 150 SheepholeValley  30 34.20661 -115.7199  596.3432        6
## 300 2017 150 SheepholeValley  30 34.20661 -115.7199  596.3432        6
## 301 2017 151          Tecopa   1 35.85152 -116.1867  699.4567        7
## 302 2017 151          Tecopa   1 35.85152 -116.1867  699.4567        7
## 303 2017 152          Tecopa   2 35.85145 -116.1867  699.5779        7
## 304 2017 152          Tecopa   2 35.85145 -116.1867  699.5779        7
## 305 2017 153          Tecopa   3 35.85141 -116.1867  700.2591        7
## 306 2017 153          Tecopa   3 35.85141 -116.1867  700.2591        7
## 307 2017 154          Tecopa   4 35.85134 -116.1866  700.8936        7
## 308 2017 154          Tecopa   4 35.85134 -116.1866  700.8936        7
## 309 2017 155          Tecopa   5 35.85130 -116.1866  700.1472        7
## 310 2017 155          Tecopa   5 35.85130 -116.1866  700.1472        7
## 311 2017 156          Tecopa   6 35.85127 -116.1866  699.6806        7
## 312 2017 156          Tecopa   6 35.85127 -116.1866  699.6806        7
## 313 2017 157          Tecopa   7 35.85121 -116.1866  700.7536        7
## 314 2017 157          Tecopa   7 35.85121 -116.1866  700.7536        7
## 315 2017 158          Tecopa   8 35.85118 -116.1865  700.9216        7
## 316 2017 158          Tecopa   8 35.85118 -116.1865  700.9216        7
## 317 2017 159          Tecopa   9 35.85117 -116.1864  702.2092        7
## 318 2017 159          Tecopa   9 35.85117 -116.1864  702.2092        7
## 319 2017 160          Tecopa  10 35.85127 -116.1863  702.9650        7
## 320 2017 160          Tecopa  10 35.85127 -116.1863  702.9650        7
## 321 2017 161          Tecopa  11 35.85123 -116.1862  702.8064        7
## 322 2017 161          Tecopa  11 35.85123 -116.1862  702.8064        7
## 323 2017 162          Tecopa  12 35.85118 -116.1863  704.1033        7
## 324 2017 162          Tecopa  12 35.85118 -116.1863  704.1033        7
## 325 2017 163          Tecopa  13 35.85115 -116.1864  704.3740        7
## 326 2017 163          Tecopa  13 35.85115 -116.1864  704.3740        7
## 327 2017 164          Tecopa  14 35.85108 -116.1865  705.4656        7
## 328 2017 164          Tecopa  14 35.85108 -116.1865  705.4656        7
## 329 2017 165          Tecopa  15 35.85108 -116.1866  705.0178        7
## 330 2017 165          Tecopa  15 35.85108 -116.1866  705.0178        7
## 331 2017 166          Tecopa  16 35.85102 -116.1866  706.5200        7
## 332 2017 166          Tecopa  16 35.85102 -116.1866  706.5200        7
## 333 2017 167          Tecopa  17 35.85116 -116.1867  706.6413        7
## 334 2017 167          Tecopa  17 35.85116 -116.1867  706.6413        7
## 335 2017 168          Tecopa  18 35.85123 -116.1867  707.5930        7
## 336 2017 168          Tecopa  18 35.85123 -116.1867  707.5930        7
## 337 2017 169          Tecopa  19 35.85124 -116.1868  707.6583        7
## 338 2017 169          Tecopa  19 35.85124 -116.1868  707.6583        7
## 339 2017 170          Tecopa  20 35.85130 -116.1869  445.7271        7
## 340 2017 170          Tecopa  20 35.85130 -116.1869  445.7271        7
## 341 2017 171          Tecopa  21 35.85133 -116.1868  446.2497        7
## 342 2017 171          Tecopa  21 35.85133 -116.1868  446.2497        7
## 343 2017 172          Tecopa  22 35.85140 -116.1869  447.1734        7
## 344 2017 172          Tecopa  22 35.85140 -116.1869  447.1734        7
## 345 2017 173          Tecopa  23 35.85145 -116.1869  447.4533        7
## 346 2017 173          Tecopa  23 35.85145 -116.1869  447.4533        7
## 347 2017 174          Tecopa  24 35.85146 -116.1868  448.7690        7
## 348 2017 174          Tecopa  24 35.85146 -116.1868  448.7690        7
## 349 2017 175          Tecopa  25 35.85150 -116.1868  448.7130        7
## 350 2017 175          Tecopa  25 35.85150 -116.1868  448.7130        7
## 351 2017 176          Tecopa  26 35.85152 -116.1869  449.0116        7
## 352 2017 176          Tecopa  26 35.85152 -116.1869  449.0116        7
## 353 2017 177          Tecopa  27 35.85147 -116.1869  447.8639        7
## 354 2017 177          Tecopa  27 35.85147 -116.1869  447.8639        7
## 355 2017 178          Tecopa  28 35.85153 -116.1870  449.0209        7
## 356 2017 178          Tecopa  28 35.85153 -116.1870  449.0209        7
## 357 2017 179          Tecopa  29 35.85168 -116.1870  448.9182        7
## 358 2017 179          Tecopa  29 35.85168 -116.1870  448.9182        7
## 359 2017 180          Tecopa  30 35.85101 -116.1872  449.0209        7
## 360 2017 180          Tecopa  30 35.85101 -116.1872  449.0209        7
## 361 2017 181      TejonRanch   1 34.87599 -118.6025 1118.0220        3
## 362 2017 181      TejonRanch   1 34.87599 -118.6025 1118.0220        3
## 363 2017 182      TejonRanch   2 34.87595 -118.6025 1118.0500        3
## 364 2017 182      TejonRanch   2 34.87595 -118.6025 1118.0500        3
## 365 2017 183      TejonRanch   3 34.87593 -118.6025 1117.6300        3
## 366 2017 183      TejonRanch   3 34.87593 -118.6025 1117.6300        3
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## 368 2017 184      TejonRanch   4 34.87593 -118.6025 1117.5090        3
## 369 2017 185      TejonRanch   5 34.87589 -118.6025 1117.4340        3
## 370 2017 185      TejonRanch   5 34.87589 -118.6025 1117.4340        3
## 371 2017 186      TejonRanch   6 34.87584 -118.6025 1117.3410        3
## 372 2017 186      TejonRanch   6 34.87584 -118.6025 1117.3410        3
## 373 2017 187      TejonRanch   7 34.87583 -118.6024 1117.2010        3
## 374 2017 187      TejonRanch   7 34.87583 -118.6024 1117.2010        3
## 375 2017 188      TejonRanch   8 34.87582 -118.6024 1116.5110        3
## 376 2017 188      TejonRanch   8 34.87582 -118.6024 1116.5110        3
## 377 2017 189      TejonRanch   9 34.87574 -118.6023 1115.8020        3
## 378 2017 189      TejonRanch   9 34.87574 -118.6023 1115.8020        3
## 379 2017 190      TejonRanch  10 34.87552 -118.6022 1114.5140        3
## 380 2017 190      TejonRanch  10 34.87552 -118.6022 1114.5140        3
## 381 2017 191      TejonRanch  11 34.87600 -118.6027 1116.3710        3
## 382 2017 191      TejonRanch  11 34.87600 -118.6027 1116.3710        3
## 383 2017 192      TejonRanch  12 34.87607 -118.6027 1116.9490        3
## 384 2017 192      TejonRanch  12 34.87607 -118.6027 1116.9490        3
## 385 2017 193      TejonRanch  13 34.87604 -118.6028 1117.4340        3
## 386 2017 193      TejonRanch  13 34.87604 -118.6028 1117.4340        3
## 387 2017 194      TejonRanch  14 34.87608 -118.6028 1117.6300        3
## 388 2017 194      TejonRanch  14 34.87608 -118.6028 1117.6300        3
## 389 2017 195      TejonRanch  15 34.87612 -118.6026 1117.1640        3
## 390 2017 195      TejonRanch  15 34.87612 -118.6026 1117.1640        3
## 391 2017 196      TejonRanch  16 34.87618 -118.6027 1117.7140        3
## 392 2017 196      TejonRanch  16 34.87618 -118.6027 1117.7140        3
## 393 2017 197      TejonRanch  17 34.87619 -118.6026 1117.7420        3
## 394 2017 197      TejonRanch  17 34.87619 -118.6026 1117.7420        3
## 395 2017 198      TejonRanch  18 34.87619 -118.6026 1118.2090        3
## 396 2017 198      TejonRanch  18 34.87619 -118.6026 1118.2090        3
## 397 2017 199      TejonRanch  19 34.87620 -118.6026 1118.0880        3
## 398 2017 199      TejonRanch  19 34.87620 -118.6026 1118.0880        3
## 399 2017 200      TejonRanch  20 34.87606 -118.6026 1117.5560        3
## 400 2017 200      TejonRanch  20 34.87606 -118.6026 1117.5560        3
## 401 2017 201      TejonRanch  21 34.87646 -118.6018 1117.7609        3
## 402 2017 201      TejonRanch  21 34.87646 -118.6018 1117.7609        3
## 403 2017 202      TejonRanch  22 34.87641 -118.6017 1116.6505        3
## 404 2017 202      TejonRanch  22 34.87641 -118.6017 1116.6505        3
## 405 2017 203      TejonRanch  23 34.87638 -118.6018 1117.1451        3
## 406 2017 203      TejonRanch  23 34.87638 -118.6018 1117.1451        3
## 407 2017 204      TejonRanch  24 34.87639 -118.6018 1117.3970        3
## 408 2017 204      TejonRanch  24 34.87639 -118.6018 1117.3970        3
## 409 2017 205      TejonRanch  25 34.87640 -118.6018 1117.1824        3
## 410 2017 205      TejonRanch  25 34.87640 -118.6018 1117.1824        3
## 411 2017 206      TejonRanch  26 34.87633 -118.6019 1115.8668        3
## 412 2017 206      TejonRanch  26 34.87633 -118.6019 1115.8668        3
## 413 2017 207      TejonRanch  27 34.87628 -118.6019 1116.7532        3
## 414 2017 207      TejonRanch  27 34.87628 -118.6019 1116.7532        3
## 415 2017 208      TejonRanch  28 34.87625 -118.6020 1116.4640        3
## 416 2017 208      TejonRanch  28 34.87625 -118.6020 1116.4640        3
## 417 2017 209      TejonRanch  29 34.87627 -118.6020 1116.5480        3
## 418 2017 209      TejonRanch  29 34.87627 -118.6020 1116.5480        3
## 419 2017 210      TejonRanch  30 34.87623 -118.6020 1117.2290        3
## 420 2017 210      TejonRanch  30 34.87623 -118.6020 1117.2290        3
## 421 2016   1    PanocheHills   1 36.70001 -120.8011  656.3209        1
## 422 2016   1    PanocheHills   1 36.70001 -120.8011  656.3209        1
## 423 2016   2    PanocheHills   2 36.70005 -120.8012  656.1902        1
## 424 2016   2    PanocheHills   2 36.70005 -120.8012  656.1902        1
## 425 2016   3    PanocheHills   3 36.70010 -120.8015  656.6475        1
## 426 2016   3    PanocheHills   3 36.70010 -120.8015  656.6475        1
## 427 2016   4    PanocheHills   4 36.70012 -120.8014  657.3752        1
## 428 2016   4    PanocheHills   4 36.70012 -120.8014  657.3752        1
## 429 2016   5    PanocheHills   5 36.70019 -120.8016  656.5075        1
## 430 2016   5    PanocheHills   5 36.70019 -120.8016  656.5075        1
## 431 2016   6    PanocheHills   6 36.70026 -120.8014  655.5837        1
## 432 2016   6    PanocheHills   6 36.70026 -120.8014  655.5837        1
## 433 2016   7    PanocheHills   7 36.70037 -120.8014  655.4344        1
## 434 2016   7    PanocheHills   7 36.70037 -120.8014  655.4344        1
## 435 2016   8    PanocheHills   8 36.70045 -120.8015  655.3132        1
## 436 2016   8    PanocheHills   8 36.70045 -120.8015  655.3132        1
## 437 2016   9    PanocheHills   9 36.70052 -120.8015  655.0519        1
## 438 2016   9    PanocheHills   9 36.70052 -120.8015  655.0519        1
## 439 2016  10    PanocheHills  10 36.70044 -120.8016  655.5371        1
## 440 2016  10    PanocheHills  10 36.70044 -120.8016  655.5371        1
## 441 2016  11    PanocheHills  11 36.70057 -120.8017  656.4329        1
## 442 2016  11    PanocheHills  11 36.70057 -120.8017  656.4329        1
## 443 2016  12    PanocheHills  12 36.70063 -120.8019  657.0300        1
## 444 2016  12    PanocheHills  12 36.70063 -120.8019  657.0300        1
## 445 2016  13    PanocheHills  13 36.70060 -120.8020  657.1606        1
## 446 2016  13    PanocheHills  13 36.70060 -120.8020  657.1606        1
## 447 2016  14    PanocheHills  14 36.70064 -120.8017  656.7687        1
## 448 2016  14    PanocheHills  14 36.70064 -120.8017  656.7687        1
## 449 2016  15    PanocheHills  15 36.70064 -120.8022  657.2166        1
## 450 2016  15    PanocheHills  15 36.70064 -120.8022  657.2166        1
## 451 2016  16    PanocheHills  16 36.70074 -120.8022  656.9554        1
## 452 2016  16    PanocheHills  16 36.70074 -120.8022  656.9554        1
## 453 2016  17    PanocheHills  17 36.70086 -120.8022  656.0223        1
## 454 2016  17    PanocheHills  17 36.70086 -120.8022  656.0223        1
## 455 2016  18    PanocheHills  18 36.70080 -120.8023  656.5541        1
## 456 2016  18    PanocheHills  18 36.70080 -120.8023  656.5541        1
## 457 2016  19    PanocheHills  19 36.70073 -120.8024  656.1996        1
## 458 2016  19    PanocheHills  19 36.70073 -120.8024  656.1996        1
## 459 2016  20    PanocheHills  20 36.70069 -120.8023  655.4438        1
## 460 2016  20    PanocheHills  20 36.70069 -120.8023  655.4438        1
## 461 2016  21    PanocheHills  21 36.70053 -120.8023  655.8543        1
## 462 2016  21    PanocheHills  21 36.70053 -120.8023  655.8543        1
## 463 2016  22    PanocheHills  22 36.70048 -120.8021  657.1420        1
## 464 2016  22    PanocheHills  22 36.70048 -120.8021  657.1420        1
## 465 2016  23    PanocheHills  23 36.70042 -120.8021  657.7485        1
## 466 2016  23    PanocheHills  23 36.70042 -120.8021  657.7485        1
## 467 2016  24    PanocheHills  24 36.70036 -120.8020  658.2523        1
## 468 2016  24    PanocheHills  24 36.70036 -120.8020  658.2523        1
## 469 2016  25    PanocheHills  25 36.70035 -120.8019  658.7189        1
## 470 2016  25    PanocheHills  25 36.70035 -120.8019  658.7189        1
## 471 2016  26    PanocheHills  26 36.70026 -120.8020  658.9055        1
## 472 2016  26    PanocheHills  26 36.70026 -120.8020  658.9055        1
## 473 2016  27    PanocheHills  27 36.70013 -120.8020  658.9988        1
## 474 2016  27    PanocheHills  27 36.70013 -120.8020  658.9988        1
## 475 2016  28    PanocheHills  28 36.70015 -120.8020  659.0361        1
## 476 2016  28    PanocheHills  28 36.70015 -120.8020  659.0361        1
## 477 2016  29    PanocheHills  29 36.70009 -120.8019  658.9614        1
## 478 2016  29    PanocheHills  29 36.70009 -120.8019  658.9614        1
## 479 2016  30    PanocheHills  30 36.70005 -120.8019  659.1201        1
## 480 2016  30    PanocheHills  30 36.70005 -120.8019  659.1201        1
## 481 2016  31          Cuyama   1 34.85522 -119.4885  806.8343        2
## 482 2016  31          Cuyama   1 34.85522 -119.4885  806.8343        2
## 483 2016  32          Cuyama   2 34.85518 -119.4886  806.5264        2
## 484 2016  32          Cuyama   2 34.85518 -119.4886  806.5264        2
## 485 2016  33          Cuyama   3 34.85512 -119.4886  806.4984        2
## 486 2016  33          Cuyama   3 34.85512 -119.4886  806.4984        2
## 487 2016  34          Cuyama   4 34.85506 -119.4885  806.6196        2
## 488 2016  34          Cuyama   4 34.85506 -119.4885  806.6196        2
## 489 2016  35          Cuyama   5 34.85505 -119.4884  807.0115        2
## 490 2016  35          Cuyama   5 34.85505 -119.4884  807.0115        2
## 491 2016  36          Cuyama   6 34.85500 -119.4884  806.0505        2
## 492 2016  36          Cuyama   6 34.85500 -119.4884  806.0505        2
## 493 2016  37          Cuyama   7 34.85489 -119.4885  808.0753        2
## 494 2016  37          Cuyama   7 34.85489 -119.4885  808.0753        2
## 495 2016  38          Cuyama   8 34.85488 -119.4884  808.7097        2
## 496 2016  38          Cuyama   8 34.85488 -119.4884  808.7097        2
## 497 2016  39          Cuyama   9 34.85489 -119.4883  808.5231        2
## 498 2016  39          Cuyama   9 34.85489 -119.4883  808.5231        2
## 499 2016  40          Cuyama  10 34.85476 -119.4884  809.9227        2
## 500 2016  40          Cuyama  10 34.85476 -119.4884  809.9227        2
## 501 2016  41          Cuyama  11 34.85462 -119.4885  809.3256        2
## 502 2016  41          Cuyama  11 34.85462 -119.4885  809.3256        2
## 503 2016  42          Cuyama  12 34.85465 -119.4884  809.5961        2
## 504 2016  42          Cuyama  12 34.85465 -119.4884  809.5961        2
## 505 2016  43          Cuyama  13 34.85454 -119.4884  810.1840        2
## 506 2016  43          Cuyama  13 34.85454 -119.4884  810.1840        2
## 507 2016  44          Cuyama  14 34.85442 -119.4884  809.8107        2
## 508 2016  44          Cuyama  14 34.85442 -119.4884  809.8107        2
## 509 2016  45          Cuyama  15 34.85441 -119.4882  810.0720        2
## 510 2016  45          Cuyama  15 34.85441 -119.4882  810.0720        2
## 511 2016  46          Cuyama  16 34.85436 -119.4881  810.3986        2
## 512 2016  46          Cuyama  16 34.85436 -119.4881  810.3986        2
## 513 2016  47          Cuyama  17 34.85443 -119.4880  810.0161        2
## 514 2016  47          Cuyama  17 34.85443 -119.4880  810.0161        2
## 515 2016  48          Cuyama  18 34.85449 -119.4880  811.1917        2
## 516 2016  48          Cuyama  18 34.85449 -119.4880  811.1917        2
## 517 2016  49          Cuyama  19 34.85461 -119.4879  811.6675        2
## 518 2016  49          Cuyama  19 34.85461 -119.4879  811.6675        2
## 519 2016  50          Cuyama  20 34.85472 -119.4878  809.8854        2
## 520 2016  50          Cuyama  20 34.85472 -119.4878  809.8854        2
## 521 2016  51          Cuyama  21 34.85472 -119.4877  810.4172        2
## 522 2016  51          Cuyama  21 34.85472 -119.4877  810.4172        2
## 523 2016  52          Cuyama  22 34.85484 -119.4876  811.3410        2
## 524 2016  52          Cuyama  22 34.85484 -119.4876  811.3410        2
## 525 2016  53          Cuyama  23 34.85492 -119.4874  810.9584        2
## 526 2016  53          Cuyama  23 34.85492 -119.4874  810.9584        2
## 527 2016  54          Cuyama  24 34.85496 -119.4873  811.6302        2
## 528 2016  54          Cuyama  24 34.85496 -119.4873  811.6302        2
## 529 2016  55          Cuyama  25 34.85500 -119.4874  811.9008        2
## 530 2016  55          Cuyama  25 34.85500 -119.4874  811.9008        2
## 531 2016  56          Cuyama  26 34.85510 -119.4875  812.1807        2
## 532 2016  56          Cuyama  26 34.85510 -119.4875  812.1807        2
## 533 2016  57          Cuyama  27 34.85511 -119.4876  812.1248        2
## 534 2016  57          Cuyama  27 34.85511 -119.4876  812.1248        2
## 535 2016  58          Cuyama  28 34.85508 -119.4876  812.6193        2
## 536 2016  58          Cuyama  28 34.85508 -119.4876  812.6193        2
## 537 2016  59          Cuyama  29 34.85499 -119.4879  811.4623        2
## 538 2016  59          Cuyama  29 34.85499 -119.4879  811.4623        2
## 539 2016  60          Cuyama  30 34.85502 -119.4881  811.7795        2
## 540 2016  60          Cuyama  30 34.85502 -119.4881  811.7795        2
## 541 2016  61         Barstow   1 35.09405 -116.8349  496.0200        4
## 542 2016  61         Barstow   1 35.09405 -116.8349  496.0200        4
## 543 2016  62         Barstow   2 35.09391 -116.8348  496.1690        4
## 544 2016  62         Barstow   2 35.09391 -116.8348  496.1690        4
## 545 2016  63         Barstow   3 35.09381 -116.8349  495.4130        4
## 546 2016  63         Barstow   3 35.09381 -116.8349  495.4130        4
## 547 2016  64         Barstow   4 35.09373 -116.8350  495.4600        4
## 548 2016  64         Barstow   4 35.09373 -116.8350  495.4600        4
## 549 2016  65         Barstow   5 35.09372 -116.8350  494.9840        4
## 550 2016  65         Barstow   5 35.09372 -116.8350  494.9840        4
## 551 2016  66         Barstow   6 35.09361 -116.8350  494.7600        4
## 552 2016  66         Barstow   6 35.09361 -116.8350  494.7600        4
## 553 2016  67         Barstow   7 35.09359 -116.8351  495.0870        4
## 554 2016  67         Barstow   7 35.09359 -116.8351  495.0870        4
## 555 2016  68         Barstow   8 35.09358 -116.8351  495.1050        4
## 556 2016  68         Barstow   8 35.09358 -116.8351  495.1050        4
## 557 2016  69         Barstow   9 35.09353 -116.8351  495.1520        4
## 558 2016  69         Barstow   9 35.09353 -116.8351  495.1520        4
## 559 2016  70         Barstow  10 35.09342 -116.8350  494.9280        4
## 560 2016  70         Barstow  10 35.09342 -116.8350  494.9280        4
## 561 2016  71         Barstow  11 35.09338 -116.8352  494.8630        4
## 562 2016  71         Barstow  11 35.09338 -116.8352  494.8630        4
## 563 2016  72         Barstow  12 35.09336 -116.8351  494.9000        4
## 564 2016  72         Barstow  12 35.09336 -116.8351  494.9000        4
## 565 2016  73         Barstow  13 35.09332 -116.8351  494.8910        4
## 566 2016  73         Barstow  13 35.09332 -116.8351  494.8910        4
## 567 2016  74         Barstow  14 35.09325 -116.8352  494.5830        4
## 568 2016  74         Barstow  14 35.09325 -116.8352  494.5830        4
## 569 2016  75         Barstow  15 35.09318 -116.8352  495.0680        4
## 570 2016  75         Barstow  15 35.09318 -116.8352  495.0680        4
## 571 2016  76         Barstow  16 35.09313 -116.8352  494.8810        4
## 572 2016  76         Barstow  16 35.09313 -116.8352  494.8810        4
## 573 2016  77         Barstow  17 35.09310 -116.8353  494.6010        4
## 574 2016  77         Barstow  17 35.09310 -116.8353  494.6010        4
## 575 2016  78         Barstow  18 35.09299 -116.8352  494.1160        4
## 576 2016  78         Barstow  18 35.09299 -116.8352  494.1160        4
## 577 2016  79         Barstow  19 35.09289 -116.8352  493.9670        4
## 578 2016  79         Barstow  19 35.09289 -116.8352  493.9670        4
## 579 2016  80         Barstow  20 35.09276 -116.8352  493.7340        4
## 580 2016  80         Barstow  20 35.09276 -116.8352  493.7340        4
## 581 2016  81         Barstow  21 35.09275 -116.8351  573.0444        4
## 582 2016  81         Barstow  21 35.09275 -116.8351  573.0444        4
## 583 2016  82         Barstow  22 35.09271 -116.8350  572.1393        4
## 584 2016  82         Barstow  22 35.09271 -116.8350  572.1393        4
## 585 2016  83         Barstow  23 35.09280 -116.8349  572.9885        4
## 586 2016  83         Barstow  23 35.09280 -116.8349  572.9885        4
## 587 2016  84         Barstow  24 35.09275 -116.8348  572.3166        4
## 588 2016  84         Barstow  24 35.09275 -116.8348  572.3166        4
## 589 2016  85         Barstow  25 35.09280 -116.8347  571.7568        4
## 590 2016  85         Barstow  25 35.09280 -116.8347  571.7568        4
## 591 2016  86         Barstow  26 35.09287 -116.8348  571.9061        4
## 592 2016  86         Barstow  26 35.09287 -116.8348  571.9061        4
## 593 2016  87         Barstow  27 35.09290 -116.8346  571.6915        4
## 594 2016  87         Barstow  27 35.09290 -116.8346  571.6915        4
## 595 2016  88         Barstow  28 35.09295 -116.8345  571.9341        4
## 596 2016  88         Barstow  28 35.09295 -116.8345  571.9341        4
## 597 2016  89         Barstow  29 35.09302 -116.8347  571.4862        4
## 598 2016  89         Barstow  29 35.09302 -116.8347  571.4862        4
## 599 2016  90         Barstow  30 35.09311 -116.8347  571.7195        4
## 600 2016  90         Barstow  30 35.09311 -116.8347  571.7195        4
## 601 2016  91   HeartofMojave   1 34.69820 -115.6842  784.7300        5
## 602 2016  91   HeartofMojave   1 34.69820 -115.6842  784.7300        5
## 603 2016  92   HeartofMojave   2 34.69811 -115.6841  784.6460        5
## 604 2016  92   HeartofMojave   2 34.69811 -115.6841  784.6460        5
## 605 2016  93   HeartofMojave   3 34.69805 -115.6841  784.8510        5
## 606 2016  93   HeartofMojave   3 34.69805 -115.6841  784.8510        5
## 607 2016  94   HeartofMojave   4 34.69798 -115.6841  784.8330        5
## 608 2016  94   HeartofMojave   4 34.69798 -115.6841  784.8330        5
## 609 2016  95   HeartofMojave   5 34.69792 -115.6841  784.5530        5
## 610 2016  95   HeartofMojave   5 34.69792 -115.6841  784.5530        5
## 611 2016  96   HeartofMojave   6 34.69794 -115.6843  784.2630        5
## 612 2016  96   HeartofMojave   6 34.69794 -115.6843  784.2630        5
## 613 2016  97   HeartofMojave   7 34.69789 -115.6843  784.3660        5
## 614 2016  97   HeartofMojave   7 34.69789 -115.6843  784.3660        5
## 615 2016  98   HeartofMojave   8 34.69784 -115.6844  784.2350        5
## 616 2016  98   HeartofMojave   8 34.69784 -115.6844  784.2350        5
## 617 2016  99   HeartofMojave   9 34.69782 -115.6843  783.9930        5
## 618 2016  99   HeartofMojave   9 34.69782 -115.6843  783.9930        5
## 619 2016 100   HeartofMojave  10 34.69779 -115.6844  783.2460        5
## 620 2016 100   HeartofMojave  10 34.69779 -115.6844  783.2460        5
## 621 2016 101   HeartofMojave  11 34.69776 -115.6843  783.0600        5
## 622 2016 101   HeartofMojave  11 34.69776 -115.6843  783.0600        5
## 623 2016 102   HeartofMojave  12 34.69772 -115.6844  783.0410        5
## 624 2016 102   HeartofMojave  12 34.69772 -115.6844  783.0410        5
## 625 2016 103   HeartofMojave  13 34.69762 -115.6844  783.1720        5
## 626 2016 103   HeartofMojave  13 34.69762 -115.6844  783.1720        5
## 627 2016 104   HeartofMojave  14 34.69764 -115.6845  782.6680        5
## 628 2016 104   HeartofMojave  14 34.69764 -115.6845  782.6680        5
## 629 2016 105   HeartofMojave  15 34.69756 -115.6844  782.5840        5
## 630 2016 105   HeartofMojave  15 34.69756 -115.6844  782.5840        5
## 631 2016 106   HeartofMojave  16 34.69747 -115.6843  781.8750        5
## 632 2016 106   HeartofMojave  16 34.69747 -115.6843  781.8750        5
## 633 2016 107   HeartofMojave  17 34.69735 -115.6843  783.0130        5
## 634 2016 107   HeartofMojave  17 34.69735 -115.6843  783.0130        5
## 635 2016 108   HeartofMojave  18 34.69730 -115.6843  782.3690        5
## 636 2016 108   HeartofMojave  18 34.69730 -115.6843  782.3690        5
## 637 2016 109   HeartofMojave  19 34.69718 -115.6843  781.3430        5
## 638 2016 109   HeartofMojave  19 34.69718 -115.6843  781.3430        5
## 639 2016 110   HeartofMojave  20 34.69714 -115.6843  781.0820        5
## 640 2016 110   HeartofMojave  20 34.69714 -115.6843  781.0820        5
## 641 2016 111   HeartofMojave  21 34.69721 -115.6842  770.7805        5
## 642 2016 111   HeartofMojave  21 34.69721 -115.6842  770.7805        5
## 643 2016 112   HeartofMojave  22 34.69729 -115.6841  770.9671        5
## 644 2016 112   HeartofMojave  22 34.69729 -115.6841  770.9671        5
## 645 2016 113   HeartofMojave  23 34.69745 -115.6838  773.9343        5
## 646 2016 113   HeartofMojave  23 34.69745 -115.6838  773.9343        5
## 647 2016 114   HeartofMojave  24 34.69748 -115.6838  773.0945        5
## 648 2016 114   HeartofMojave  24 34.69748 -115.6838  773.0945        5
## 649 2016 115   HeartofMojave  25 34.69748 -115.6837  773.9996        5
## 650 2016 115   HeartofMojave  25 34.69748 -115.6837  773.9996        5
## 651 2016 116   HeartofMojave  26 34.69755 -115.6837  774.5034        5
## 652 2016 116   HeartofMojave  26 34.69755 -115.6837  774.5034        5
## 653 2016 117   HeartofMojave  27 34.69761 -115.6836  774.8766        5
## 654 2016 117   HeartofMojave  27 34.69761 -115.6836  774.8766        5
## 655 2016 118   HeartofMojave  28 34.69764 -115.6836  775.0259        5
## 656 2016 118   HeartofMojave  28 34.69764 -115.6836  775.0259        5
## 657 2016 119   HeartofMojave  29 34.69769 -115.6837  774.9606        5
## 658 2016 119   HeartofMojave  29 34.69769 -115.6837  774.9606        5
## 659 2016 120   HeartofMojave  30 34.69772 -115.6837  775.0726        5
## 660 2016 120   HeartofMojave  30 34.69772 -115.6837  775.0726        5
## 661 2016 121 SheepholeValley   1 34.20568 -115.7197  545.9200        6
## 662 2016 121 SheepholeValley   1 34.20568 -115.7197  545.9200        6
## 663 2016 122 SheepholeValley   2 34.20574 -115.7195  546.5730        6
## 664 2016 122 SheepholeValley   2 34.20574 -115.7195  546.5730        6
## 665 2016 123 SheepholeValley   3 34.20565 -115.7194  546.6290        6
## 666 2016 123 SheepholeValley   3 34.20565 -115.7194  546.6290        6
## 667 2016 124 SheepholeValley   4 34.20559 -115.7192  546.2470        6
## 668 2016 124 SheepholeValley   4 34.20559 -115.7192  546.2470        6
## 669 2016 125 SheepholeValley   5 34.20563 -115.7192  546.5170        6
## 670 2016 125 SheepholeValley   5 34.20563 -115.7192  546.5170        6
## 671 2016 126 SheepholeValley   6 34.20558 -115.7190  546.2930        6
## 672 2016 126 SheepholeValley   6 34.20558 -115.7190  546.2930        6
## 673 2016 127 SheepholeValley   7 34.20561 -115.7190  545.8920        6
## 674 2016 127 SheepholeValley   7 34.20561 -115.7190  545.8920        6
## 675 2016 128 SheepholeValley   8 34.20555 -115.7190  545.7430        6
## 676 2016 128 SheepholeValley   8 34.20555 -115.7190  545.7430        6
## 677 2016 129 SheepholeValley   9 34.20572 -115.7188  546.5080        6
## 678 2016 129 SheepholeValley   9 34.20572 -115.7188  546.5080        6
## 679 2016 130 SheepholeValley  10 34.20577 -115.7187  546.9090        6
## 680 2016 130 SheepholeValley  10 34.20577 -115.7187  546.9090        6
## 681 2016 131 SheepholeValley  11 34.20580 -115.7186  547.1330        6
## 682 2016 131 SheepholeValley  11 34.20580 -115.7186  547.1330        6
## 683 2016 132 SheepholeValley  12 34.20594 -115.7186  547.8890        6
## 684 2016 132 SheepholeValley  12 34.20594 -115.7186  547.8890        6
## 685 2016 133 SheepholeValley  13 34.20611 -115.7187  548.4580        6
## 686 2016 133 SheepholeValley  13 34.20611 -115.7187  548.4580        6
## 687 2016 134 SheepholeValley  14 34.20626 -115.7190  549.5310        6
## 688 2016 134 SheepholeValley  14 34.20626 -115.7190  549.5310        6
## 689 2016 135 SheepholeValley  15 34.20632 -115.7192  550.0070        6
## 690 2016 135 SheepholeValley  15 34.20632 -115.7192  550.0070        6
## 691 2016 136 SheepholeValley  16 34.20631 -115.7194  550.3800        6
## 692 2016 136 SheepholeValley  16 34.20631 -115.7194  550.3800        6
## 693 2016 137 SheepholeValley  17 34.20628 -115.7196  549.9420        6
## 694 2016 137 SheepholeValley  17 34.20628 -115.7196  549.9420        6
## 695 2016 138 SheepholeValley  18 34.20625 -115.7196  550.1560        6
## 696 2016 138 SheepholeValley  18 34.20625 -115.7196  550.1560        6
## 697 2016 139 SheepholeValley  19 34.20627 -115.7197  549.4560        6
## 698 2016 139 SheepholeValley  19 34.20627 -115.7197  549.4560        6
## 699 2016 140 SheepholeValley  20 34.20618 -115.7198  549.1950        6
## 700 2016 140 SheepholeValley  20 34.20618 -115.7198  549.1950        6
## 701 2016 141 SheepholeValley  21 34.20650 -115.7194  598.3586        6
## 702 2016 141 SheepholeValley  21 34.20650 -115.7194  598.3586        6
## 703 2016 142 SheepholeValley  22 34.20652 -115.7192  597.8641        6
## 704 2016 142 SheepholeValley  22 34.20652 -115.7192  597.8641        6
## 705 2016 143 SheepholeValley  23 34.20664 -115.7191  599.4130        6
## 706 2016 143 SheepholeValley  23 34.20664 -115.7191  599.4130        6
## 707 2016 144 SheepholeValley  24 34.20678 -115.7192  600.1594        6
## 708 2016 144 SheepholeValley  24 34.20678 -115.7192  600.1594        6
## 709 2016 145 SheepholeValley  25 34.20664 -115.7192  597.9014        6
## 710 2016 145 SheepholeValley  25 34.20664 -115.7192  597.9014        6
## 711 2016 146 SheepholeValley  26 34.20659 -115.7193  597.4069        6
## 712 2016 146 SheepholeValley  26 34.20659 -115.7193  597.4069        6
## 713 2016 147 SheepholeValley  27 34.20659 -115.7195  595.8207        6
## 714 2016 147 SheepholeValley  27 34.20659 -115.7195  595.8207        6
## 715 2016 148 SheepholeValley  28 34.20658 -115.7198  595.4101        6
## 716 2016 148 SheepholeValley  28 34.20658 -115.7198  595.4101        6
## 717 2016 149 SheepholeValley  29 34.20655 -115.7199  595.5687        6
## 718 2016 149 SheepholeValley  29 34.20655 -115.7199  595.5687        6
## 719 2016 150 SheepholeValley  30 34.20661 -115.7199  596.3432        6
## 720 2016 150 SheepholeValley  30 34.20661 -115.7199  596.3432        6
## 721 2016 151          Tecopa   1 35.85152 -116.1867  699.4567        7
## 722 2016 151          Tecopa   1 35.85152 -116.1867  699.4567        7
## 723 2016 152          Tecopa   2 35.85145 -116.1867  699.5779        7
## 724 2016 152          Tecopa   2 35.85145 -116.1867  699.5779        7
## 725 2016 153          Tecopa   3 35.85141 -116.1867  700.2591        7
## 726 2016 153          Tecopa   3 35.85141 -116.1867  700.2591        7
## 727 2016 154          Tecopa   4 35.85134 -116.1866  700.8936        7
## 728 2016 154          Tecopa   4 35.85134 -116.1866  700.8936        7
## 729 2016 155          Tecopa   5 35.85130 -116.1866  700.1472        7
## 730 2016 155          Tecopa   5 35.85130 -116.1866  700.1472        7
## 731 2016 156          Tecopa   6 35.85127 -116.1866  699.6806        7
## 732 2016 156          Tecopa   6 35.85127 -116.1866  699.6806        7
## 733 2016 157          Tecopa   7 35.85121 -116.1866  700.7536        7
## 734 2016 157          Tecopa   7 35.85121 -116.1866  700.7536        7
## 735 2016 158          Tecopa   8 35.85118 -116.1865  700.9216        7
## 736 2016 158          Tecopa   8 35.85118 -116.1865  700.9216        7
## 737 2016 159          Tecopa   9 35.85117 -116.1864  702.2092        7
## 738 2016 159          Tecopa   9 35.85117 -116.1864  702.2092        7
## 739 2016 160          Tecopa  10 35.85127 -116.1863  702.9650        7
## 740 2016 160          Tecopa  10 35.85127 -116.1863  702.9650        7
## 741 2016 161          Tecopa  11 35.85123 -116.1862  702.8064        7
## 742 2016 161          Tecopa  11 35.85123 -116.1862  702.8064        7
## 743 2016 162          Tecopa  12 35.85118 -116.1863  704.1033        7
## 744 2016 162          Tecopa  12 35.85118 -116.1863  704.1033        7
## 745 2016 163          Tecopa  13 35.85115 -116.1864  704.3740        7
## 746 2016 163          Tecopa  13 35.85115 -116.1864  704.3740        7
## 747 2016 164          Tecopa  14 35.85108 -116.1865  705.4656        7
## 748 2016 164          Tecopa  14 35.85108 -116.1865  705.4656        7
## 749 2016 165          Tecopa  15 35.85108 -116.1866  705.0178        7
## 750 2016 165          Tecopa  15 35.85108 -116.1866  705.0178        7
## 751 2016 166          Tecopa  16 35.85102 -116.1866  706.5200        7
## 752 2016 166          Tecopa  16 35.85102 -116.1866  706.5200        7
## 753 2016 167          Tecopa  17 35.85116 -116.1867  706.6413        7
## 754 2016 167          Tecopa  17 35.85116 -116.1867  706.6413        7
## 755 2016 168          Tecopa  18 35.85123 -116.1867  707.5930        7
## 756 2016 168          Tecopa  18 35.85123 -116.1867  707.5930        7
## 757 2016 169          Tecopa  19 35.85124 -116.1868  707.6583        7
## 758 2016 169          Tecopa  19 35.85124 -116.1868  707.6583        7
## 759 2016 170          Tecopa  20 35.85130 -116.1869  445.7271        7
## 760 2016 170          Tecopa  20 35.85130 -116.1869  445.7271        7
## 761 2016 171          Tecopa  21 35.85133 -116.1868  446.2497        7
## 762 2016 171          Tecopa  21 35.85133 -116.1868  446.2497        7
## 763 2016 172          Tecopa  22 35.85140 -116.1869  447.1734        7
## 764 2016 172          Tecopa  22 35.85140 -116.1869  447.1734        7
## 765 2016 173          Tecopa  23 35.85145 -116.1869  447.4533        7
## 766 2016 173          Tecopa  23 35.85145 -116.1869  447.4533        7
## 767 2016 174          Tecopa  24 35.85146 -116.1868  448.7690        7
## 768 2016 174          Tecopa  24 35.85146 -116.1868  448.7690        7
## 769 2016 175          Tecopa  25 35.85150 -116.1868  448.7130        7
## 770 2016 175          Tecopa  25 35.85150 -116.1868  448.7130        7
## 771 2016 176          Tecopa  26 35.85152 -116.1869  449.0116        7
## 772 2016 176          Tecopa  26 35.85152 -116.1869  449.0116        7
## 773 2016 177          Tecopa  27 35.85147 -116.1869  447.8639        7
## 774 2016 177          Tecopa  27 35.85147 -116.1869  447.8639        7
## 775 2016 178          Tecopa  28 35.85153 -116.1870  449.0209        7
## 776 2016 178          Tecopa  28 35.85153 -116.1870  449.0209        7
## 777 2016 179          Tecopa  29 35.85168 -116.1870  448.9182        7
## 778 2016 179          Tecopa  29 35.85168 -116.1870  448.9182        7
## 779 2016 180          Tecopa  30 35.85101 -116.1872  449.0209        7
## 780 2016 180          Tecopa  30 35.85101 -116.1872  449.0209        7
## 781 2016 181      TejonRanch   1 34.87599 -118.6025 1118.0220        3
## 782 2016 181      TejonRanch   1 34.87599 -118.6025 1118.0220        3
## 783 2016 182      TejonRanch   2 34.87595 -118.6025 1118.0500        3
## 784 2016 182      TejonRanch   2 34.87595 -118.6025 1118.0500        3
## 785 2016 183      TejonRanch   3 34.87593 -118.6025 1117.6300        3
## 786 2016 183      TejonRanch   3 34.87593 -118.6025 1117.6300        3
## 787 2016 184      TejonRanch   4 34.87593 -118.6025 1117.5090        3
## 788 2016 184      TejonRanch   4 34.87593 -118.6025 1117.5090        3
## 789 2016 185      TejonRanch   5 34.87589 -118.6025 1117.4340        3
## 790 2016 185      TejonRanch   5 34.87589 -118.6025 1117.4340        3
## 791 2016 186      TejonRanch   6 34.87584 -118.6025 1117.3410        3
## 792 2016 186      TejonRanch   6 34.87584 -118.6025 1117.3410        3
## 793 2016 187      TejonRanch   7 34.87583 -118.6024 1117.2010        3
## 794 2016 187      TejonRanch   7 34.87583 -118.6024 1117.2010        3
## 795 2016 188      TejonRanch   8 34.87582 -118.6024 1116.5110        3
## 796 2016 188      TejonRanch   8 34.87582 -118.6024 1116.5110        3
## 797 2016 189      TejonRanch   9 34.87574 -118.6023 1115.8020        3
## 798 2016 189      TejonRanch   9 34.87574 -118.6023 1115.8020        3
## 799 2016 190      TejonRanch  10 34.87552 -118.6022 1114.5140        3
## 800 2016 190      TejonRanch  10 34.87552 -118.6022 1114.5140        3
## 801 2016 191      TejonRanch  11 34.87600 -118.6027 1116.3710        3
## 802 2016 191      TejonRanch  11 34.87600 -118.6027 1116.3710        3
## 803 2016 192      TejonRanch  12 34.87607 -118.6027 1116.9490        3
## 804 2016 192      TejonRanch  12 34.87607 -118.6027 1116.9490        3
## 805 2016 193      TejonRanch  13 34.87604 -118.6028 1117.4340        3
## 806 2016 193      TejonRanch  13 34.87604 -118.6028 1117.4340        3
## 807 2016 194      TejonRanch  14 34.87608 -118.6028 1117.6300        3
## 808 2016 194      TejonRanch  14 34.87608 -118.6028 1117.6300        3
## 809 2016 195      TejonRanch  15 34.87612 -118.6026 1117.1640        3
## 810 2016 195      TejonRanch  15 34.87612 -118.6026 1117.1640        3
## 811 2016 196      TejonRanch  16 34.87618 -118.6027 1117.7140        3
## 812 2016 196      TejonRanch  16 34.87618 -118.6027 1117.7140        3
## 813 2016 197      TejonRanch  17 34.87619 -118.6026 1117.7420        3
## 814 2016 197      TejonRanch  17 34.87619 -118.6026 1117.7420        3
## 815 2016 198      TejonRanch  18 34.87619 -118.6026 1118.2090        3
## 816 2016 198      TejonRanch  18 34.87619 -118.6026 1118.2090        3
## 817 2016 199      TejonRanch  19 34.87620 -118.6026 1118.0880        3
## 818 2016 199      TejonRanch  19 34.87620 -118.6026 1118.0880        3
## 819 2016 200      TejonRanch  20 34.87606 -118.6026 1117.5560        3
## 820 2016 200      TejonRanch  20 34.87606 -118.6026 1117.5560        3
## 821 2016 201      TejonRanch  21 34.87646 -118.6018 1117.7609        3
## 822 2016 201      TejonRanch  21 34.87646 -118.6018 1117.7609        3
## 823 2016 202      TejonRanch  22 34.87641 -118.6017 1116.6505        3
## 824 2016 202      TejonRanch  22 34.87641 -118.6017 1116.6505        3
## 825 2016 203      TejonRanch  23 34.87638 -118.6018 1117.1451        3
## 826 2016 203      TejonRanch  23 34.87638 -118.6018 1117.1451        3
## 827 2016 204      TejonRanch  24 34.87639 -118.6018 1117.3970        3
## 828 2016 204      TejonRanch  24 34.87639 -118.6018 1117.3970        3
## 829 2016 205      TejonRanch  25 34.87640 -118.6018 1117.1824        3
## 830 2016 205      TejonRanch  25 34.87640 -118.6018 1117.1824        3
## 831 2016 206      TejonRanch  26 34.87633 -118.6019 1115.8668        3
## 832 2016 206      TejonRanch  26 34.87633 -118.6019 1115.8668        3
## 833 2016 207      TejonRanch  27 34.87628 -118.6019 1116.7532        3
## 834 2016 207      TejonRanch  27 34.87628 -118.6019 1116.7532        3
## 835 2016 208      TejonRanch  28 34.87625 -118.6020 1116.4640        3
## 836 2016 208      TejonRanch  28 34.87625 -118.6020 1116.4640        3
## 837 2016 209      TejonRanch  29 34.87627 -118.6020 1116.5480        3
## 838 2016 209      TejonRanch  29 34.87627 -118.6020 1116.5480        3
## 839 2016 210      TejonRanch  30 34.87623 -118.6020 1117.2290        3
## 840 2016 210      TejonRanch  30 34.87623 -118.6020 1117.2290        3
##     Microsite Abundance Richness  Biomass B.rubens E.cicutarium
## 1       shrub       221        1 11.87950      221            0
## 2        open       175        3  1.99860      133           39
## 3       shrub       192        1 16.03870      192            0
## 4        open       161        4  5.64030      115           42
## 5       shrub       167        1  9.92170      167            0
## 6        open       142        2  2.02090       92           50
## 7       shrub       112        1  5.72630      112            0
## 8        open       143        3  3.28260       80           59
## 9       shrub       179        1  8.69420      179            0
## 10       open       162        3  3.22120      136           22
## 11      shrub       212        1 18.61760      212            0
## 12       open       146        3  3.55210      105           39
## 13      shrub       241        1 14.57810      241            0
## 14       open       141        3  5.38070       93           45
## 15      shrub        77        2 10.68060       73            4
## 16       open       129        2  3.63750      108           21
## 17      shrub        92        1 14.87970       92            0
## 18       open       131        2  3.12750       87           44
## 19      shrub       211        1 15.24860      211            0
## 20       open       205        3  4.96610      176           28
## 21      shrub       245        1 12.55180      245            0
## 22       open       185        2  5.86360       97           88
## 23      shrub       250        1 22.53730      250            0
## 24       open       163        3  7.35210      112           49
## 25      shrub        21        2 10.90710        9           12
## 26       open       170        2  4.29460      162            8
## 27      shrub       244        1 22.46610      244            0
## 28       open       128        3  2.91640      105           21
## 29      shrub       264        1 15.14000      264            0
## 30       open       131        4  9.43150       98           30
## 31      shrub       266        1 15.05400      266            0
## 32       open       116        3  6.90250       91           22
## 33      shrub       211        1 11.88910      211            0
## 34       open       169        2  6.70100      140           29
## 35      shrub       260        1 19.13290      260            0
## 36       open       143        2  1.39980      112           31
## 37      shrub       170        1 12.41270      170            0
## 38       open       136        2  2.37420      101           35
## 39      shrub       198        1 15.27590      198            0
## 40       open       148        2  2.08760      140            8
## 41      shrub       310        1 34.22070      310            0
## 42       open       129        3  2.87600       15           28
## 43      shrub       286        1 11.89440      286            0
## 44       open       117        3 14.68390       59           30
## 45      shrub       296        1 18.59040      296            0
## 46       open       140        3  4.32140      108           29
## 47      shrub       258        2 11.01730      256            0
## 48       open       165        4 13.20240      110           10
## 49      shrub       266        1 32.47440      266            0
## 50       open       134        2  7.64740      105           29
## 51      shrub       240        1 28.16850      240            0
## 52       open       186        2  3.88680       98           88
## 53      shrub       286        1 10.63740      286            0
## 54       open       172        3  3.50420       98           62
## 55      shrub       217        1 17.04760      217            0
## 56       open       200        3  4.71760      104           91
## 57      shrub       268        2 14.45670      263            0
## 58       open       137        2  2.36400       98           39
## 59      shrub       219        2 12.60390      214            5
## 60       open       116        2  4.86980       56           60
## 61      shrub       256        1  8.58120      256            0
## 62       open        91        5  1.65260        8            3
## 63      shrub       208        1 10.66130      208            0
## 64       open       132        2  1.11850        0            7
## 65      shrub       268        2 14.34510      243            0
## 66       open       103        3  3.21740       23            9
## 67      shrub        83        1  7.22370       83            0
## 68       open        87        3  0.88640        0            6
## 69      shrub       171        1  5.77210      171            0
## 70       open       116        4  6.87320        0           23
## 71      shrub        39        2  1.47820       28           11
## 72       open        83        3  1.16510        0           23
## 73      shrub       174        3 16.38720      163            3
## 74       open       107        3  0.94850        0           10
## 75      shrub       106        1  8.11320      106            0
## 76       open        52        4  0.25080        0            4
## 77      shrub         8        1  1.48020        8            0
## 78       open        68        3  0.19280        0            4
## 79      shrub       165        2  3.98620      163            0
## 80       open       102        3  1.00770        0           12
## 81      shrub       211        1  4.30930      211            0
## 82       open       109        3  1.82970        0           28
## 83      shrub       110        2  6.50120      108            0
## 84       open       118        3  2.18930        0           49
## 85      shrub        67        2  3.77440       63            4
## 86       open        82        3  1.39520        0            9
## 87      shrub        50        2  2.81670       48            2
## 88       open        62        3  1.05490        0           45
## 89      shrub        90        1  5.07010       90            0
## 90       open        77        3  1.31010        0            5
## 91      shrub        88        2  4.95740       83            0
## 92       open        50        3  0.85070        0            8
## 93      shrub        30        3  1.69000       11            9
## 94       open        42        3  0.71460        0           31
## 95      shrub       182        2 10.25280      165            0
## 96       open        49        3  0.83370        0           17
## 97      shrub       114        3  6.42210      107            0
## 98       open       141        3  2.39910        0           17
## 99      shrub       110        1  6.19670      110            0
## 100      open       160        4  2.72240        4           51
## 101     shrub       133        2  7.49240      112            0
## 102      open        95        4  1.61640        0           11
## 103     shrub       122        1  6.87270      122            0
## 104      open       119        4  2.02480        3           10
## 105     shrub        58        1  3.26740       58            0
## 106      open        55        3  0.93580        0            8
## 107     shrub        29        2  1.63370       26            0
## 108      open        62        4  1.05490        0            8
## 109     shrub        60        1  3.38000       60            0
## 110      open        71        2  1.20810        0            0
## 111     shrub        81        1  4.56300       81            0
## 112      open        88        3  1.49730        0            3
## 113     shrub        53        1  2.98570       53            0
## 114      open        76        3  1.29310        0            4
## 115     shrub        35        3  1.97170       31            1
## 116      open        96        3  1.63340        0            4
## 117     shrub       115        1  6.47840      115            0
## 118      open        63        4  1.07190        8            6
## 119     shrub       117        2  6.59110      115            0
## 120      open        63        3  1.07190        0            8
## 121     shrub        20        2  1.83530        0            0
## 122      open        23        2  0.15060        0            0
## 123     shrub        66        3  0.74410        0            0
## 124      open        13        1  0.08190        0            0
## 125     shrub        23        3  2.71840        0            0
## 126      open        39        1  0.27830        0            0
## 127     shrub        21        2  1.82420        0            0
## 128      open         9        3  0.21370        0            0
## 129     shrub        11        3  1.66470        0            0
## 130      open        21        1  0.66770        0            0
## 131     shrub         9        1  1.78140        0            0
## 132      open        10        2  0.58200        0            0
## 133     shrub         6        2  1.77240        0            0
## 134      open        13        2  0.74630        0            0
## 135     shrub        13        2  0.50750        0            0
## 136      open        14        1  0.10040        0            0
## 137     shrub        22        3  2.24130        0            0
## 138      open        14        2  0.87430        0            0
## 139     shrub        16        1  0.52610        0            0
## 140      open        23        1  0.30270        0            0
## 141     shrub        21        3  1.81080        0            0
## 142      open        11        3  0.67980        0            0
## 143     shrub        17        1  0.61790        0            0
## 144      open        18        3  0.45470        0            0
## 145     shrub         3        2  0.50040        0            0
## 146      open        24        2  0.14130        0            0
## 147     shrub        14        1  2.93890        0            0
## 148      open        12        1  0.20780        0            0
## 149     shrub         8        2  4.20920        0            0
## 150      open        14        3  0.70520        0            3
## 151     shrub        17        2  0.75770        0            0
## 152      open        23        2  0.22760        0            0
## 153     shrub        14        1  4.85360        0            0
## 154      open        18        3  0.15070        0            2
## 155     shrub        14        2  0.70310        0            0
## 156      open        15        1  0.26790        0            0
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##     M.lanceolata D.capitatum C.brevipes L.decorus P.insularis M.glabrata
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##     L.gracilis A.tessellata A.grandiflora O.deltoides Boraginaceae.sp.
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##     P.secunda E.glyptosperma B.diandrus M.affinis L.sparsiflorus
## 1           0              0          0         0              0
## 2           0              0          0         0              0
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## 647             0              0
## 648             0              0
## 649             0              0
## 650             0              0
## 651             0              0
## 652             0              0
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## 654             0              0
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## 674             0              0
## 675             0              0
## 676             0              0
## 677             0              0
## 678             0              0
## 679             0              0
## 680             0              0
## 681             0              0
## 682             0              0
## 683             0              0
## 684             0              0
## 685             0              0
## 686             0              0
## 687             0              0
## 688             0              0
## 689             0              0
## 690             0              0
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## 692             0              0
## 693             0              0
## 694             0              0
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## 700             0              0
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## 733             0              0
## 734             0              0
## 735             0              0
## 736             0              0
## 737             0              0
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## 743             0              0
## 744             0              0
## 745             0              0
## 746             0              0
## 747             0              0
## 748             0              0
## 749             0              0
## 750             0              0
## 751             0              0
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## 753             0              0
## 754             0              0
## 755             0              0
## 756             0              0
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## 764             0              0
## 765             0              0
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## 767             0              0
## 768             0              0
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## 772             0              0
## 773             0              0
## 774             0              0
## 775             0              0
## 776             0              0
## 777             0              0
## 778             0              0
## 779             0              0
## 780             0              0
## 781             0              0
## 782             0              0
## 783             0              0
## 784             0              0
## 785             0              0
## 786             0              0
## 787             0              0
## 788             0              0
## 789             0              0
## 790             0              0
## 791             0              0
## 792             0              0
## 793             0              0
## 794             0              0
## 795             0              0
## 796             0              0
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## 800             0              0
## 801             0              0
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## 819             0              0
## 820             0              0
## 821             0              0
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## 823             0              0
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## 834             0              0
## 835             0              0
## 836             0              0
## 837             0              0
## 838             0              0
## 839             0              0
## 840             0              0
aggregate(Richness~1, FUN=mean, data=community)
##   Richness
## 1 2.228571